A Brief History Of Data Science

Data science is a relatively new field in the world of academia. The earliest known use of data science dates back to 1854 when Charles Babbage wrote about the use of statistical sampling methods in his book “On the Economy of Machinery and Manufactures”.

Data science began to take off in the 1960s with the development of computers and databases.

The data science is heavily based on statistics and the use of statistical models. Data science began with statistics and has since expanded to include artificial intelligence, machine learning, and the Internet of Things.

Businesses have been gathering and keeping data in ever increasing numbers as more data has been made available, first through recorded shopping behaviors and trends.

Data scientists are primarily responsible for finding patterns and trends in large datasets. They also develop algorithms that help companies make better decisions.

Data science has quietly developed, incorporating enterprises and organizations all across the world in the last thirty years. Governments, geneticists, engineers, and even astronomers are now using it.

Importance Of Data Science

Data science has become more important in recent years due to its ability to provide insights from data. It can provide insight into how companies are performing or what their customers want. Data science can also be used for predicting future events by using predictive analytics.

Data science helps us make sense of the huge amount of information we have at our disposal. It helps us understand what this information means and how to make use of it for better decision-making.

Data science is one of the most important fields in business. It can provide a lot of value to businesses.

Data science enables businesses to get insights from huge amounts of data, which helps them make better decisions and improve their products.

Companies can analyze trends to make important decisions that will improve consumer interest, company success, and profitability.

With the help of data science, recruiters can identify candidates that best fit the company’s needs.

Data science helps recruiters by combining data points to identify candidates that best fit their company’s needs. For example, a recruiter can use data to find out what traits are most important for a candidate to have in order to be successful at a company. This allows them to focus on recruiting for specific positions and not waste time on candidates who don’t fit their requirements.

In the IT industry, data science has been a growth area. It’s becoming a big part of the IT industry that helps firms become more successful and efficient.

The generation and deployment of information is a vital economic activity in today’s world. It is made easier by Data Science’s ability to extract information from massive amounts of data. Information technology makes our lives easier by allowing us to collect and process more data more quickly and efficiently, allowing us to get results in hours rather than days or weeks.

Data Science Scope As A Good Career In India & Job Opportunities

According to AIM Research, there were 137,870 data science jobs available in India in June 2021, representing a 47.1 percent increase in open positions compared to June 2020. 

Data science has seen tremendous growth in the past few years in India and has become one of the most sought-after career options for college graduates as well as career switchers.

The scope of data science includes several fields such as statistics, machine learning, data mining, artificial intelligence, etc., which broadens career opportunities.

Data science can be a good career path if you are interested in using data and statistics in order to understand and solve problems.

Data science is a relatively new field in India. The number of people working in this field has been increasing rapidly because of the explosion of data creation in every sector, from finance and banking to retail.

Data science job opportunities have increased significantly over the last few years, making it an attractive career option for people looking for a change from their current careers.

In India, there are many job opportunities in the data science domain. There are several companies that offer lucrative salaries to people who have studied in this field or those who want to enter the sector for their career development.

The job opportunities for data science graduates are vast. Some of the top companies offering data science jobs in India are Amazon, Google, Microsoft, Facebook, Uber, etc.

Currently, India accounts for 9.4% of all global analytics job vacancies, up from 7.2 percent in January 2020.

Data science is a promising career option, but you need to be prepared for the high competition. There are many organizations that hire data scientists, but it’s important to find a company that meets your needs—whether it’s for your personal growth or for the company’s growth.

What Are The Jobs After The Data Science Course?

Experts in data science are required in almost every industry, not only technology.
In these current years, data science is one of the thriving fields in a wide range of businesses around the world. In this fast-paced world, information is becoming increasingly crucial, and this has created a lot of opportunities for information-driven jobs in different organizations.

The following are some of the most well-known Data Science job titles

Data Scientist

Salary

As per the PayScale survey, in India, the starting salary for a data scientist is ₹42,000.

Data scientists with five to nine years of experience can earn between ₹12 and ₹14 lakhs per year.

A data scientist in the United States earns an average of $100,560 per year.

Roles & Responsibilities

A data scientist plays an important role in data analysis, which begins with data collection and ends with business decisions based on the data scientist’s data analytics conclusions.

Data Architect

Salary

• As per Indeed.com In India, a Data Architect’s average monthly income is ₹23,536.
• The average annual salary in the United States is $100,000.

Roles & Responsibilities

• They want to provide data management systems for organizations that will allow them to integrate, protect, and maintain data sources and information. Database architecture, design, construction, and data optimization are all their responsibilities.

Data Analyst

Salary

In India, the range of income ranges from 1.8 lakhs to 11.5 lakhs for data analysts with less than one year of experience to six years of experience. (Source: Ambition Box)

In the United States, data analysts earn an average of $62,000 a year. (Source: Indiacsr)

Roles & Responsibilities

The data analyst is responsible for gathering, processing, and analyzing statistical data.
The data analyst identifies useful information and extracts it using R or SAS. There is a need for data analysts in many fields, such as healthcare, autos, finance, retail, and insurance.

Business Analyst

Salary

The compensation of a business analyst in India ranges from 3.0 lakhs to 15.2 lakhs for those with less than a year of experience to 9 years. (Source: Ambition Box)

In the United States, data The average compensation for this data science job in the United States is $65,000.

Roles & Responsibilities

The role of a business analyst is similar to that of a data analyst. Business analysts, on the other hand, have specialized knowledge of the industry. They put their skills to work to improve the company’s products and services. They gather and assess the organization’s basic business demands and needs, and they are responsible for managing change.

How To Get A Job In Data Science As A Fresher?

Data science is a field that has seen exponential growth in the past few years and it is likely to grow even more in the future. There are many opportunities for people who have the right skillset. But how do you acquire those skills?

If you are the type of person who needs a mentor to guide you, push you in the right direction, and help you when you are not able to complete the course.

Lead you step by step through each new topic and the advanced topics (and sometimes even basic ones!)

The result is that you get the right skill set that is important for grabbing the job opportunity, and you can easily get the job.

Then “live interactive classroom training” is suited for you! 

The second step is to find a job that you are interested in. Look for companies or organizations that have data science jobs and contact them. Then You can also look on social media websites, such as LinkedIn, or search for jobs on websites like Indeed or Glassdoor.

(And if you’re interested in attending a free trial of recorded live sessions that gives an overview of the Data Science Course, then you can contact us on 8600995107 or 7028710777.)

The following are some of the skill sets that you should have when applying for data science jobs:
➤ Machine Learning: This involves using algorithms and statistical models to discover patterns in data. It is one of the most sought-after skill sets in data science.
➤ Data Visualization: These are tools that help extract insights from large datasets by making them more manageable and accessible with interactive graphics, charts, graphs, maps, etc.
➤ Algorithms: These are mathematical formulas that help solve problems in different fields such as business, engineering, economics, etc.

Does Data Science Require Coding?

This question has been debated on whether data science requires coding skills or not. Some people argue that it doesn’t, while others argue that it does. Some even argue that data science requires both programming skills and statistical knowledge.

The truth is, there are no fixed requirements for becoming a data scientist, and you can choose the path that best suits your interests.

If you want to develop data science applications, then you require coding, a deep understanding of software engineering, and advanced programming.

Over its 30 years of existence, Python has developed some great libraries to deal with data science applications. One of the prime reasons that Python is loved by the scientific and research communities is its ease of use and simple syntax. What makes it easy to adapt for people who do not have a software engineering background.

It has simple syntax & vocabulary to pick up relatively quickly, especially compared to more complex languages like C++, java.

Fundamentals Of Data Science / Data Science Concepts

Data science is an interdisciplinary field that combines concepts from statistics, computer programming, mathematics, and engineering. Data scientists are the ones who use this information to make predictions and decisions about the future based on past data patterns.

Data Science Fundamentals:

➤Math for data science 

➤Data collection and storage

➤Data processing

➤EDA (Exploratory Data Analysis) in Data Science

➤Modeling

➤Prediction and decision-making

➤Machine Learning

Math for data science

Data science is a field that requires the use of mathematics in order to extract insights from data. 

The world is changing quickly with the advancements in technology. With the use of data science, businesses and individuals can make better decisions and gain a competitive edge.

This article will discuss some of the math topics that are important for data science. It will also provide some examples of how they are used in data science.

Math Topics

Basic Statistics: The most basic form of statistics is to calculate the mean, median, mode, and standard deviation.

Probability: Proportions are usually expressed as a percentage or a fraction.

Functions: Functions are used to describe relationships between different variables or to find patterns in data sets. They can be linear or nonlinear functions.

EDA (Exploratory Data Analysis) in Data Science

Exploratory Data Analysis is the process of exploring and visualizing data to understand its behavior in order to make informed decisions.
EDA is a useful tool for data scientists because it helps them to discover patterns, outliers, and trends in their data. It also helps them identify anomalies and missing values.

It is not easy to examine a column of numbers or an entire spreadsheet and determine essential aspects of the data. It may be difficult, boring, or overwhelming to extract insights by looking at basic numbers. In this case, exploratory data analysis techniques have been developed to help.

EDA has become an integral part of many data science projects because it provides insight into the behavior of the data and helps make informed decisions about what to do with it.

There are two ways to categorize exploratory data analysis. The first point is that each method is either non-graphical or graphical. Second, each method is univariate or multivariate in nature (usually just bivariate).

Data scientists use EDA to make sure that the results they give are correct, can be correctly interpreted, and can be used in the business contexts they want.

What Is Data Wrangling?

➤ Gathering of data from diverse sources to reveal “deeper intelligence.”

➤ In a timely manner, deliver reliable, actionable data to business analysts.

➤ Reduce the amount of time it takes to collect and organise disorderly data before using it.

➤ Allow data scientists and analysts to concentrate on data analysis rather than data wrangling.

The Basics of Data Wrangling

Data Acquisition: Find and acquire access to the data included in your sources.

Joining Data: Combine the modified data for further use and analysis.

Data Cleaning: Redesign the data in a usable and organized manner and remove any bad data.

Hypothesis testing in data science

Hypothesis testing is a statistical technique that helps to determine the probability of an event. It is used in many fields, especially in data science.

Hypothesis testing is a method in statistics that helps us decide whether our data is valid and reliable.

It is important to understand the basics of hypothesis testing so that you can conduct your own hypothesis tests in your data science projects.

Parameters & Statistics In Hypothesis testing

A parameter is a description of a population in statistics, whereas a statistic is a description of a small subset of a population (sample). 

If you question everyone in your class (the population) about their average height, you’ll get a parameter, which is a genuine representation of the population because everyone was asked. 

This information becomes a statistic if you wish to determine the average height of people in your grade (population) based on the information you have from your class (sample).

Parametric tests are hypothesis tests that include a specified parameter. The population in parametric testing is assumed to have a normal distribution (e.g., the height of people in a class).

The Basic Steps of Hypothesis Testing

There are many steps to testing a hypothesis, such as

1. Null & Alternative hypothesis

The null and alternative hypotheses are two statements that are mutually exclusive. The null hypothesis states that no impact or difference exists. The alternative hypothesis is what you want to prove.

2. Selection of an appropriate test statistic

To put your assertions to the test, you must select the appropriate test or test statistic. The t-test, z-test, and F-test are all commonly used tests that assume a normal distribution. In business, however, a normal distribution is rarely assumed. 

3. Data Collection

A random sample of the true population of interest is required to conduct a hypothesis test. To eliminate bias or undesired consequences, the sample should be chosen at random.

Data Visualization In Data Science

Data visualization is an important part of data science and helps to bring the data to life. It can help a person understand the data better by using visual representations.

Data visualization is also a key component of Big Data and it helps in making sense of large amounts of information. Data visualization can be used in various ways, such as creating interactive dashboards, creating infographics, and presenting results in a more attractive way.

It is a tool that can be used by data scientists to present their results or insights, as well as for business analysts, marketers, and journalists.
Data Visualization is required for understanding datasets that are too large or too complex for human beings to analyze on their own.

What are the steps followed in Data Visualization?

The following are some of the most important steps in of Data Visualization in Data Science

Data Cleaning

Data Science Visualization can help in the detection of null values in large datasets by representing them differently. This reduces the load of finding mistakes before dealing with data for professionals.

Data Exploration

Data visualization involves the creation of visualizations that can help people find information or help them identify patterns in their own data.

Modeling

Machine Learning Models for Predictive Analysis are created by Data Scientists. To improve these models, they are trained with enormous datasets. When the models are being trained, the results are analyzed using Data Visualization Tools to see how well they are performing and where they are lacking.

Identifying Trends

Real-time data is sometimes used by data scientists and data analysts to derive relevant trends. Real-time data is challenging to examine since it is always changing. For a better understanding, the data might be displayed using charts and graphs.

Data Preprocessing In Data Science

Data preprocessing is the first step in data science. It includes the collection of raw data, cleaning it up for analysis, and transforming it into a form that can be analyzed. There are many steps involved in preprocessing, which include:

• Collection of raw data
• Cleaning up raw data for analysis
• Transforming raw data into a form that can be analyzed

Probability For Data Science

Probability is a branch of mathematics that deals with the analysis and application of random variables. It can be understood as a measure of the chance that something will happen.

It is used in statistics, game theory, and other fields to quantify the chance that an event will occur.

The most important part of data science is data analysis, which includes probability for data science and statistical inference.
Probability is not just about understanding what is happening, but also about calculating and understanding how likely it is to happen.

The formula for probability is,

P(A) = Number of favorable outcome / Total number of favorable outcome

Data Science Process/Methodology

Data Science is a process that helps individuals and organizations extract, clean, analyze and visualize data.

The primary goal of data science is to find patterns in large amounts of data. It can also be used for forecasting future outcomes or predicting the behavior of individuals based on their social media posts or credit card purchases.

The procedure for discovering solutions to a specific problem is referred to as “Data Science Methodology.” This is a cyclic process that goes through a critical phase, directing business analysts and data scientists to take the proper actions.

Understanding Of The Business

Any problem in the business domain must be thoroughly understood before it can be solved. Understanding business creates an advantage, which makes it easier to solve problems. We should have a clear understanding of the problem we are attempting to solve.

Understanding The Analytic Process

The analytical method to use should be determined based on the given business information.
There are four types of approaches: descriptive (current status and information given), diagnostic (a.k.a. statistical analysis, what is occurring and why it is occurring); predictive (it predicts trends or future event probability), and prescriptive ( how the issue should be resolved in reality).

Requirements For Data

The above mentioned analytical process identifies the required data content, formats,    and sources. During the data requirements process, one should find the answers to questions like “what”, “where”, “when”, “why”, “how” and “who”.

Data Collection

The data collected can be retrieved in any format. So, the data acquired should be validated in keeping with the approach chosen and the desired output. As a result, if more data is needed, it can be gathered or the useless data can be discarded.

Understanding the Data

“Is the data collected representative of the problem to be solved?” is the question that data understanding answers. Descriptive statistics determine the measures that are applied to data in order to determine the content and quality of the data. This step could result in a reversion to the prior stage for correction.

Data Preparation

Let’s look at two analogies to help us understand this topic. The first is to wash freshly picked vegetables, and the second is to just take the desired foods to eat from the buffet. Vegetable washing denotes the removal of dirt, i.e. unwanted items, from data. Noise reduction is carried out here. Putting just eatable items on the plate, that is, if we don’t need specific data, then we should not consider it for further process. This whole procedure includes transformation, normalization, etc.

Modeling

Modeling determines whether the data that has been prepared for processing is suitable or if it requires additional finishing . The goal of this phase is to create predictive and descriptive models.

Evaluation

Model evaluation is done during model development. It examines the model’s quality as well as whether it fits the business’s needs. It goes through a diagnostic measure phase (to see if the model is working as intended and if any changes are needed) and a statistical significance testing phase (ensures proper data handling and interpretation).

Deployment

The deployment phase determines how well the model can handle the external environment and some models perform better than others.

Feedback

Feedback is a necessary function that helps in the development of the model as well as accessing its performance and influence. The steps involved in feedback include defining the review procedure, keeping track of the record, measuring performance, and reviewing and modifying the results.

Data Science Life Cycle

The data science life cycle is a model that describes the process of analyzing and transforming data into insights.

The Data Science life cycle is a series of steps that can be iterative and important to complete a project or data analysis. These steps can be unique, but most projects follow the same general steps.

The data science life cycle includes different stages. These stages are: data collection, preparation, modeling, deployment, and monitoring. 

With the help of these stages, it becomes easy for teams to identify opportunities and make changes in their business models.

The 6 Stages of the Data Science Life Cycle

Framing The Problem

The Problem Framing Stage is the first step in the Data Science Life Cycle. The goal of this part is to identify the problem that you want to solve and understand it.
In order to frame the problem, it is important to understand what business or research question you are trying to answer. You should also know who your audience is and how they will be impacted by your solution.

Collecting Data

After defining the problem we’re attempting to solve, we must gather data that can be used in the next steps of the problem-solving.

Here, data scientists collect and organize data. 

The Collecting Data Stage is a very important part of the Data Science Life Cycle where it is decided what type of data to collect and how to collect it.

This process can be tedious and time-consuming, but it’s important to get insights from the data gathered from various sources.

Data Processing

We need to process the data after gathering it from reliable sources.

The processing stage in the data science life cycle is where all of the raw data is transformed into a format that can be used by analysts to generate insights. This stage includes cleaning, filtering, and pre-processing.

Data Exploration

Data exploration is a step where you get to know the data before working with it. Large datasets are prepared for deeper, more structured analysis through surveys and investigation.

At this stage, we collect as much information about our dataset as possible. We also need to find out what questions we want to ask in order to make sense of our dataset.

The purpose of the data exploration step is to ensure that we can discover patterns from our data that will help us solve our problem.

Analyzing the Data

At this stage, we decide what we want to analyze and what the suitable methods are for it.
Data analysis is where we make sure that our data is in order and we are ready to make a decision or prediction with it. This stage includes checking if there are any missing values or outliers in our dataset.
In this step, we try to get a deeper understanding of the data we have collected and processed. We use statistical and numerical methods to draw inferences about the data and to identify the relationship between multiple columns in our dataset.

We can also use visualizations to better understand and summarize the data using images, graphs, charts, plots, etc.

Consolidating Results

The Consolidating Results stage is the last step in the Data Science Life Cycle. It is where the data scientist takes all of the analysis and results that they have generated and presents them in a way that is meaningful to others. They are also responsible for packaging their findings into a report, presentation, or publication.

Data science is an important part of many different industries, such as healthcare, finance, marketing, etc. With the data science life cycle being so crucial to these industries, it’s important to know exactly what it entails and how one can go about completing this process.

Clustering In Data Science

Clustering is a data science technique that groups similar data points together. This can be done by comparing the similarities of the data points and finding patterns in them.
Clustering is one of the most popular techniques used in data science and has been applied to many different industries with diverse applications such as finance, marketing, social media, and healthcare.

Types of Clustering

Clustering is classified into two categories

Hard Clustering

In hard clustering, each data point is either totally or partially associated with a cluster.
Let’s say you have a data set of 100,000 students. You want to cluster them into a smaller number of groups. In this case, you would use the hard clustering technique to find the best cluster size for your data set.

Soft Clustering

Instead of assigning each data point to a separate cluster, soft clustering assigns a chance or likelihood of that data point being in those clusters.

The idea is that, when you have a lot of similar content, the algorithm can group them together and then search for the most relevant one.

Algorithms for clustering

We will also discuss the many types of algorithms that may be used for clustering and how they can be used in different scenarios.

1. Connectivity models

The essential notion of connectivity-based clustering, also known as hierarchical clustering, is that things are more related to neighboring objects than objects further away. These algorithms use distance to connect “objects” to generate “clusters.”

2. Centroid Models

The most common type of clustering is centroid-based clustering. 

These methods divide data points into groups based on several centroids in the data. Based on its squared distance from the centroid, each data point is assigned to a cluster. This is the most popular clustering technique.

3. Distribution Models

With a distribution-based clustering method, all data points are considered to be part of a cluster based on how likely they are to be in that cluster.

It works like this: if there is a center point, the further a data point is from the center, the less likely it is to be a part of that cluster.

If you’re not sure how your data will be distributed, you should use a different type of algorithm.

4. Density Models

These models look for locations in the data space with varying densities of data points. It isolates various density regions and groups the data points inside these regions into clusters. DBSCAN and OPTICS are two popular density models

Popular Algorithms Used In Data Science

These models look for locations in the data space with varying densities of data points. It isolates various density regions and groups the data points inside these regions into clusters. DBSCAN and OPTICS are two popular density models

1. Linear Regression

Linear regression is a statistical technique for estimating and predicting a continuous outcome based on a set of independent and dependent variables. The technique fits a linear equation using the least-squares method to data points in an x-y scatter plot.

2. Logistic Regression

Logistic regression is one of the most commonly used data science models. It is mainly used to predict continuous outcomes from a set of predictor variables.

Logistic regression is a powerful tool that allows you to understand your data better and make predictions with more accuracy.

Logistic regression is a type of statistical algorithm that can be used to predict the probability of an event happening. It is also called a logit regression or a logit model. 

Ronald Fisher developed this model in 1938 and it has been widely used in many fields such as marketing, forecasting, and economics.

3. Decision Trees

Decision trees are a type of data science model used to predict the outcome of a given event. They are widely used in the industry due to their simplicity and accuracy.

Decision trees are widely used in many industries, such as marketing, finance, and healthcare. They are a great tool for making predictions about future events.

4. Random Forest

A Random Forest is a machine learning algorithm that can be used to build decision trees. It is one of the most popular algorithms in data science. This algorithm has been used in many different fields, including marketing, finance, and biology, to predict customer behavior or analyze a particular situation.

The Random Forest model is also known as the Random Decision Forest (RDF). They are used to find patterns in large datasets by building decision trees that have a large number of samples in them.

It has been seen that random forest performs better than other models in predicting outcomes with more complex structures, such as those with categorical variables or multiple dependent variables.

4. Regression Analysis

Regression analysis is a statistical technique that predicts the values of one variable based on the values of other variables.

It is one of the most important techniques that data scientists use in their work. It helps them to understand how a variable changes with time and how to make predictions about it.

Regression analysis is also used in business analytics, marketing, and healthcare to predict customer behavior and trends.

Components Of Data Science

Data science is the process of identifying patterns in data. Insight can be extracted from these patterns and used for business intelligence or the development of new product features.

Both of these data science project outcomes can be valuable to product teams wanting to differentiate their offers in the market and give more value to customers. But before your team can start to use data science, they need to know about key components of data science.

Data Science has following four main components

1. Data Strategy

Developing a data strategy simply entails deciding what data you’ll collect and why. 

If you run a business, you have data., but data simply won’t help you optimize and develop it. If you want to turn data into value, you’ll need a data strategy.

The tools, methods, and rules that specify how to manage, analyze, and act on company data are referred to as data strategy. A data strategy can assist you in making data-driven decisions. It also helps in the security and compliance of your data.

2. Data Engineering

Data engineering is a branch of data science that focuses on practical data collecting and analysis applications. Data engineers are responsible for the design, analysis, and management of these systems.

Data engineering is a very important part of data science. Big data applications and harvesting are the focus of data engineers. They don’t perform a lot of analysis or experiment design in their job. 

They could be experts in the following areas:

• ➤Architecture of the system
• ➤Programming
• ➤Configuration and design of databases
• ➤Configuration of the interface and sensors
•  

A data engineer is more knowledgeable about distributed systems and is a better coder than a data scientist. Data engineering requires a deep understanding of a wide range of data technologies and frameworks, as well as how to combine them to make solutions that support business processes through data pipelines.

3. Data Analysis

The process of analyzing, cleansing, manipulating, and modeling data with the objective of identifying usable information, informing conclusions, and assisting decision-making is known as data analysis.

Data analysis has many facets and methods, including a wide range of techniques.

Data analysis is important in today’s business environment because it helps businesses make more data driven decisions and run more efficiently.

This is the process of making sense out of a set of collected data by identifying patterns and relationships within them. It also entails generating predictions, drawing conclusions, and creating visualizations based on these findings.

4. Data Visualization

A graphical representation of information and data is referred to as “data visualization.”
Data visualization techniques make it easy to identify and comprehend trends, outliers, and patterns in data by employing visual elements like charts, graphs, and maps.
In today’s world, we have a lot of data in our hands, so data visualization tools and technologies are essential for analyzing huge volumes of data and making data-driven decisions.
Data visualization is one of the most important skills for data scientists. They need to be able to visualize their data in a way that makes it easy for non-data scientists to understand.
Data visualization can be done by using either 2D or 3D graphs, charts and maps.

It’s used in a variety of situations, including:

Visualize phenomena that you can’t see immediately, such as weather patterns, medical issues, or mathematical correlations.

Types Of Data In Data Science

Working with data is essential because we must determine what type of data we have and how to use it to get useful results.

It’s also crucial to understand which type of plot is best for which data category; this helps with data analysis and visualization.

Working with data requires strong data science abilities as well as a thorough awareness of various sorts of data and how to work with them.

In research, analysis, statistics, and data science, various forms of data are used. This information assists a business in analyzing its operations, developing strategies, and establishing an effective data-driven decision-making process.

1. Nominal Data

Nominal data is a good source of data for analysis. However, it is not the most suitable source of data for many applications.

The main challenge with nominal data is that it has a low level of statistical significance. This makes it difficult to draw conclusions from the results. The problem can be solved by using other sources of nominal data such as categorical or continuous variables (e.g., grades).

2. Ordinal Data

Nominal data is a good source of data for analysis. However, it is not the most suitable source of data for many applications.

The main challenge with nominal data is that it has a low level of statistical significance. This makes it difficult to draw conclusions from the results. The problem can be solved by using other sources of nominal data such as categorical or continuous variables (e.g., grades).

3. Discrete Data

The word “discrete” refers to anything different or separate. The discrete data is made up of values that are whole numbers or integers.
For example, discrete data can be the overall number of students in a class, which doesn’t have a decimal or fractional value.
The discrete variables can be counted and have finite values. However, they can not be subdivided.
A bar graph, number line, or frequency table are commonly used to illustrate discrete data.

4. Continuous Data

Data scientists are using continuous data to solve problems in the fields of machine learning, statistics, and data science.

Fractional numbers are used to represent continuous data. It could be the Android phone’s version, a person’s height, the length of an object, and so on. Continuous data is information that can be broken down into smaller parts. Any value within a range can be assigned to the continuous variable.

The main distinction between discrete and continuous data is that discrete data includes integers or whole numbers. Continuous data, on the other hand, uses fractions to store different kinds of information, like temperature, height, width, time, speed, and so on.

Continuous Data Examples :-

➤A person’s height

➤A vehicle’s top speed

➤”Time taken” to complete the task

➤Wi-Fi frequency

➤Market share price

Which Programming Languages Are Supported for Data Science

Programming is a method for data scientists to interact with computers and send instructions to them.

There are hundreds of programming languages available, each designed for a certain purpose. Some are better suited to data science, with great productivity and performance for processing massive amounts of data.

Following are the most popular programming languages used in the data science

1. Python

Programming is a method for data scientists to interact with computers and send instructions to them.

There are hundreds of programming languages available, each designed for a certain purpose. Some are better suited to data science, with great productivity and performance for processing massive amounts of data.

The following are the most popular Python libraries for data science and machine learning used by data scientists,

NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras & more.

2. R Programming

R is a statistical computing and graphics language and environment developed by R Foundation for Statistical Computing.
It is an open-source programming language that has gained popularity in the scientific community and has been adopted by many other computing communities.
The language is used to code in many scientific and engineering tasks, including data analysis, machine learning, and graphics.

3. SQL

SQL is an essential skill for data science.
SQL is mainly used to store, query, and manipulate data in databases.
Learning SQL enables you to combine data from multiple sources and transform complicated data sets into useful insights. Learning to write excellent SQL queries, in general, improves your logical thinking ability.

Other programming languages used for data science include

1. Java
2. Julia
3. Scala
4. C/C++
5. JavaScript
6. Swift
7. Go
8. MATLAB
9. SAS

Advantages And Disadvantages Of Data Science

Advantages of Data Science

Data is being generated at an alarming rate in today’s environment.

Every second, a large amount of data is generated, whether it comes from Facebook users or any other social networking site, phone calls, or data generated by other businesses.

The value of the discipline of data science has a variety of advantages as a result of this massive amount of data. The following are some of the benefits:

1. Data Science is Popular

Data scientists are in high demand all over the world, and the lack of competition for these positions makes data science a very lucrative career path.

By 2026, 11.5 million new data scientist jobs will be created (WEF, 2020).

In 2021, there was a need for 50,000 data scientists (India Today, 2021).

With Technogeeks’ Data Science Course, you can become a part of the great change!

Data science is not only used by large technology companies but also by startups and small businesses all over the world.

2. A Highly Paid Career

Data scientists earn an average of $116,100 a year, according to Glassdoor.

The global need for data science professionals is enormous, and this provides the foundation for competitive data science professional pay.

Data science is a highly paid career, and the demand for data scientists is expected to continue to grow.

In the future, companies may need to hire more data scientists.

3. Data Science Makes Data Better

The first step is to get a good understanding of the data. Then we can take that data and use it to make better decisions. 

Data science can help with better decision making, but also for content generation and other fields like sales, marketing, and customer service.

Data scientists are using AI tools in their work. They use AI tools to improve the quality of data and deliver insights that can help companies make better decisions.

Data scientists are in high demand by businesses to process and evaluate their data. They not only analyze but also improve the quality of the data. 

As a result, Data Science is concerned with enriching data and making it more useful to their business.

Disadvantages Of Data Science

Everything that offers a number of advantages also has some disadvantages. So, let’s have a look at a few of the disadvantages.

1. The Problem of Data Privacy in Data Science

Data is a key component that can help businesses enhance efficiency and income by allowing them to make game-changing business decisions. However, the data’s knowledge or insights can be utilized against any company, group of people, committee, or other entity. Information taken from structured and unstructured data that can be used later can be used against a group of people in a country or a committee.

2. Reliance on Domain Knowledge

Another disadvantage of data science is its reliance on domain knowledge. A person with strong experience in statistics and computer science will find it challenging to solve data science challenges without its background understanding.

The same can be said for the other way around. A healthcare industry working on genomic sequence analysis, for example, will require a suitable employee with some genetics and molecular biology understanding.

This helps the data scientists to make educated decisions in order to help the firm. However, it becomes challenging for a data scientist from a different background to acquire relevant domain knowledge. This also makes it harder to shift from one industry to another.

3. Arbitrary Data Can Produce Unexpected Results

A Data Scientist examines the data and makes careful predictions in order to help in decision-making. 

The data provided is frequently arbitrary and does not produce the expected results. 

This can also fail due to poor management and resource allocation.

4. Cost

Because some of the tools used in data science and analytics are complicated and require training to use, they can be costly to a business. 

Furthermore, choosing the ideal tools for the job is tough because it depends on having a thorough understanding of the tools as well as their accuracy in analyzing and extracting data.

What Are The Data Science Tools?

Data science tools are used by data scientists to analyze data in a way that is both efficient and effective.

These tools give you a ready-to-use array of API connectors, pre-defined functions, and algorithms over a very user-friendly GUI without using a programming language.

Data science is a vast process with no single tool that can handle the entire workflow. 

So, at different stages in the data science process, the following tools are used:

• Data Storage

• Exploratory Data Analysis

• Data Modeling

• Data Visualization

Data Science Tools For Data Storage

Data storage is a very important part of any business. Organizations need to store their data in order to make sure they are able to deliver on their promises and objectives.

The ability to store data securely and efficiently is one of the key requirements for businesses today.

Data Storage Tools help you store your data in SQL Server and other relational databases and organize and manage it.

Apache Hadoop

Apache Hadoop is a free, open-source platform that can manage and store lots and lots of data.

It allows big data sets to be distributed across a cluster of thousands of computers.

It’s used for data processing and high-level computations.

H4 – Azure HDInsight

Microsoft’s Azure HDInsight is a cloud platform that allows you to store, process, and analyze data.

Companies like Adobe, Jet and Milliman use Azure HD Insights to handle and manage huge amounts of data.

Microsoft R Server is a server that enables enterprise-scale R for statistical analysis and the creation of robust machine learning models.

Data Science Tools Exploratory Data Analysis

An exploratory data analysis tool for data science allows you to do exploratory analysis on a dataset and then generate insights.

The following are some of the most commonly used data science tools for EDA,

1. Python language

Python is used to do EDA in order to find the missing value in a batch of data.

Other tasks that can be carried out include data description, dealing with outliers, and drawing conclusions from plots.

2. R language

The R language is used to clean and provide rich resources for data analytics. It works with all the operating systems.

When paired with programming languages like C++, Java, or Python, its capabilities increase significantly.

R is the ideal tool since it has a wide range of libraries for analytics, statistical computation, and data cleansing.

R is available as a paid-for or free download. 

Other EDA tools used by the data science community are:

SAS, KNIME, DataPrep, Pandas Profiling, SweetViz, AutoViz, Microsoft Excel

Data Science Tools For Data Modeling

The process of analyzing data objects and their relationships to other objects is known as data modeling. It’s used to examine the data needs of business processes.

Data modeling helps improve naming, rules, semantics, and security.

Business regulations, regulatory compliance, and government policies are all enforced on the data during the data modeling process.

The following are some examples of data modeling tools:

1. H2O.ai

H2O.ai is a distributed in-memory machine learning platform with linear scalability that is completely open source. 

H2O.ai works with the most popular statistical and machine learning methods, such as gradient boosted machines, generalized linear models, deep learning, and more.

The goal of H20.ai is to make data modeling easier.

2. DataRobot

DataRobot is an AI-powered automation tool that assists in the creation of precise predictive models.

Data Robots are able to learn and adapt to the needs of their users by using datasets that they have been trained with.

It is a complete automation of data science tasks, such as pattern recognition, clustering, predictive analytics, machine learning, and more.

Data Science Tools For Data Visualization

Data visualization is a huge part of the modern business world. It is essential for companies to analyze data and draw conclusions from it. Data science tools are used by both end users and data scientists in their day-to-day work.

When data scientists examine large datasets, they must also understand the data obtained. Through graphs and charts, data visualization will make it easier for people to understand.

1. Tableau

Tableau is a visual data visualization tool that allows users to manipulate and analyze data in the form of charts and graphs.
It includes a drag-and-drop interface, which allows it to complete tasks quickly and effortlessly.
Although the tool is expensive, it is the chosen option of a major corporation such as Amazon.
Tableau is often regarded as the most user-friendly business intelligence application for data visualization.

2. QlikView

Qlikview is a data visualization and analysis tool. It is used for data mining and analytics.
QlikView is a piece of software that works similarly to a tableau, but it requires payment before it can be used for professional purposes.
This software aids in the process of data visualization.
QlikView is used in over 100 countries and has a huge number of users.

How To Learn Data Science?

The best way of learning data science is by doing hands-on practicals. But it may take a lot of time. Based on your natural way of learning, you can either enroll in a self-paced MOOC course.

Coursera, Udemy, or any other platform. Follow along with the course modules, solve assignments, and do all the projects that will help in deepening the knowledge you have just learned.

OR

If you are the type of person who needs a mentor to guide you, push you in the right direction, and help you when you are not able to complete the course.

They will lead you step by step through each new topic and leave the advanced topics (and sometimes even basic ones!)

Then “live interactive classroom training” is suited for you! 

(And if you’re interested in attending a free trial of recorded live sessions, then you can contact us on 8600995107 or 7028710777!)

Check out our placement record here: https://technogeekscs.com/placements

Or what our students are saying about us here: https://technogeekscs.com/testimonials

Is Data Science Hard?

Learning Data Science Is Not Hard. 

The belief that data science is difficult to understand is a common misunderstanding among beginners.

As they learn more about the unique field of data science, they realize that it is just another subject that can be learned with hard work.

Especially for freshers, they think that data science is more difficult to understand, so to better understand data science, there are many online courses that can help to learn data science.

If you are not working hard enough or are unsure about what you need to know to become a data scientist, learning data science may appear challenging.

If you’ve made up your mind, the odds are in your favor, according to the US Bureau of Labor Statistics, which estimates that 11.5 million new positions in data science will be available by 2026.

There are many opportunities for those who have mastered data science. And, by that, we don’t mean you have to be a Ph.D. or a master’s degree holder to acquire your first job as a data scientist. In fact, as per the California University of Pennsylvania, roughly 61% of the data science positions have a bachelor’s degree as the qualification.

Another thing to bear in mind is that data science jobs are only likely to grow as more businesses get interested in data-driven decision making. Data science positions are expected to grow by 28% by 2026, according to the US Bureau of Labor Statistics. So, for at least the next five years, you can put your fears about the number of jobs on hold and focus on developing your skills.

It won’t be tough for you to begin your career as a data scientist as long as you grab the right skill set.

To become a Data Science expert, one must have a strong foundation in a variety of areas, including:Python, R & SQL.

Best Data Science Companies In India

India contributed to 9.4 percent of the total global analytics job openings, a rise from 7.2 percent in January 2020. – TOI

Data Analytics is a big field in India. There are many companies that specialize in various data science aspects, and they have different approaches to it. Some of them focus on the technical aspects of it, while others focus on the business aspects of it.

The Top 10 Successful Data Science Companies in India are listed below

• Oracle Cloud Solution Hubs

• Accenture Analytics

• Fractal Analytics

• Crayon Data

• Bridgei2i

• Cartesian Consulting

• Latent View

• DataTrained

• Datalicious

• IBM

Comparing Data Science With Other Related Fields

Business Intelligence vs. Data Science

Business intelligence reflects business needs by using data (typically through dynamic reporting) to answer questions (business synthesis).

Data Science emphasizes statistical and algorithmic methods in dealing with large data sets; this is called “Data Breadth.”