Introduction to Data Science
Data Science :- Data science is a relatively new field in 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 .
Data science, which heavily depends on statistical models, originated from the area of statistics and has since expanded to enclose various technologies such as artificial intelligence, machine learning, and the Internet of Things.
Suppose I am providing you the data of an online grocery store. In that case, I can tell you which product is in demand and which is not or spread into multiple categories like customers’ previous search product, which product they like, which product they saved, whether It is male or female so on, to understand customer Requirements according to Customer search.
So You have given me some insights from that raw data. The data I had was from an online store database in unstructured forms. You have transformed the data into meaningful data, and that’s what a data scientist does in transforming the data into meaningful insights. Now it doesn’t matter what tool you are using. As long as you provide the data to me you are a data Scientist.
Why we learn Data Science
- Data Science is a rapidly growing field with increasing demand for Professional Developers who can analyze and interpret data. It enables you to turn Your data into actionable understandings, design innovative Solutions and Solve real-world problems.
- Data Science Course is such a Skill that a person from any occupation can do, like Engineering, Medicine, Pharmacy, Business, Civil, Mathematics, etc., it’s possible to lead to New and Exciting career opportunities in various industries.
- Data Science has high earning prospect and is among the top-earning professional in the world. Data Science is one of the highest-paying jobs in the tech industry, with an average salary of over $120,000 per year. Learning data science courses helps And provides opportunities tour, Career advancement, especially in areas such as Machine Learning & Artificial intelligence.
- Technological advancements and the increasing availability of data have made data analysis and modeling more important and valuable.
Applications of Data Science in different industries
- Manufacturing: In Manufacturing Predictive maintenance and supply chain optimization using data analysis.
- Finance: Using data analysis techniques, fraud detection, risk assessment, and portfolio optimization.
- Retail: Predictive analysis of sales and customer behavior to inform and optimize marketing and sales strategies.
- Healthcare: Predictive modeling and analysis of patient data to improve treatment outcomes and diagnosis.
- Energy: Predictive analysis of energy consumption patterns and demand forecasting to inform energy management and investment decisions.
- Transportation: Optimization of transportation networks and fleet management through data analysis.
- Marketing: Predictive analysis of customer behavior and market trends to inform marketing strategy and target advertising effectively.
- Technology: Predictive analysis of customer behavior and product usage to inform product development and improve customer experiences.
- Environmental Science: Analysis of environmental data to inform conservation and sustainability efforts.
- Sports: Predictive analysis of player performance and tactical decision-making using data analysis.
Ethics in Data Science
The following are some examples of data science ethics:
- Confirming that personal data is collected, stored, and used in a way that respects individuals’ privacy rights.
- Ensuring that data and algorithms are used in a responsible manner that does not cause harm to individuals or Society.
- Addressing and lightening sources of bias in data and algorithms that can result in one-sided or inaccurate outcomes.
- Make data and algorithms transparent, interpretable, and accountable so their results can be understood and trusted.
- Confirming that data ownership rights are respected, and that data is always used with individuals’ expectations.
If you’re accomplishing things with data, you must recognize that You have an excellent deal of power that comes with great responsibility. This Data Science course will teach You how to be responsible in that exercise of power.
Data Scientists who have had ethical training will result in a better, more ethical practice of data science, and I think that is good for data Science and this is good for Society at large.
What is Python?
- Python is a powerful programming language that can do much based on logic. Still, all the time, logic is behind the code. It is the most widely used and preferred language among developers because it is simple to learn and works perfectly.
- As we all know, Python is an easy and understandable language. It is a high-level, object-oriented programming language. Python is an interpreter-based and platform-friendly language. Guido van Rossum created and released it in 1991 at the CWI( Centrum Wiskundeen Informatica )in the Netherlands.
- We fell in love with programming because of the short length of code and simple syntax. With Python, data science courses can import, pure, manipulate, and analyze data, produce machine learning models, and communicate wisdom through visualizations and reports. Python’s versatility makes it suitable for solving different data science problems, from (NLP) natural language processing to computer vision, predictive modeling, and more.
- Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn provide a comprehensive set of tools for each process step. Also, Python’s versatility and scalability make it appropriate for both small and large datasets, Making it a versatile tool for data science. Whether a beginner or an experienced data scientist, learning Python for data science can open up many new career opportunities.
List of python modules
1. Data types
- Int
- Float
- String(str)
- List
- Tuple
- Boolean
- Set
- edit
2. it-elif-else
The “if-elif-else” statement in Python is a fundamental building block of programming. It provides a way to execute code based on a certain condition.
3. Loop
- A “loop” is used to do repeated tasks.
- For loop:- If you want to repeat some task fixed number of times, then you use the for loop.
- While loop:- While a condition is true it repeats a group of statements.
- Nested loop:- We can iterate a loop into another loop.
- Break; continue.
4. Function methods
In Python, a function is a reusable block of code that performs a specific task and can be called by its name. Functions are defined using the def keyword, followed by the function name and a set of parentheses that may include parameters. Functions allow you to break down your code into smaller, more manageable blocks, improve modularity and code reusability, and facilitate better program design. Functions are called by using their name followed by parentheses containing the parameters for that function.
5. Object oriented programing
- Object-oriented programming is a programming language paradigm. In OOPs, everything is represented as an object and a class rather than functions and logic. It always revolves around data.
- Oops, they are widely used in software development for many reasons. One of the main causes is the reusability of code, scalability, and efficiency.
- Oops, is used in many popular programming languages, such as Python, Java, and C++, as well as in many other programming languages.
- Object-oriented programming has four main principles :
- Encapsulation
- Abstraction
- Inheritance
- Polymorphism
6. Module and Packages
- A module is a single file that contains Python definitions and statements. Modules provide a way to structure your code, making it easier to maintain and reuse.
- A package on the other hand, is a directory that contains multiple modules. Packages provide a way to organize your modules into a directory hierarchy, making it easier to manage related modules.
7. Exception Handling
- Exceptions in Python can be handled with the try and except statements. When a Python programme comes across a condition it can’t handle, it raises an exception. In Python, an object that describes an error is called an exception.
- Type of exception handling
- Syntax error
- Logical error
- Exceptions
8. Date-time
Python has a built-in DateTime module that provides functionality for working with timestamps in your code. The time class within the DateTime module is specifically designed to represent time values and includes attributes for hour, minute, second, and microsecond. Information about the time zone may also be included. The parameters used to establish a time instance are optional, although the default value of 0 probably isn’t what you want.
9. Advanced python
- Object-Oriented Programming (OOP)
- Decorators
- Generators
- Iterators
- Context Managers
- Coroutines
- Metaclasses
- Concurrent programming with threads and multiprocessing
- Network programming
- Advanced Data Structures (e.g. NumPy, Pandas)
- Advanced Modules (e.g. os, sys, etc.)
- File I/O operations
- Exception handling
- Regular expressions
- Unit testing
- Debugging
- Advanced algorithms and design patterns
- Database programming (e.g. SQLite, MySQL, etc.)
- Web scraping and data processing
- GUI programming (e.g. Tkinter, PyQt, etc.)
10. Regular Expression
- Regular expressions are text-matching patterns described with a formal syntax. You’ll often hear regular expressions called ‘regex’ or ‘regexp’ in conversation. Regular expressions can include a variety of rules, from finding recurrence to text-matching and much more. As you advance in Python, you’ll see that many of your parsing problems can be solved with regular expressions.
- If you’re familiar with Perl, you’ll notice that the syntax for regular expressions is very similar in Python. We will use the <code>re</code> module with Python for this lecture.
What is data analytics?
📉📈 Data analytics analyzes data sets to find, collect, transform, and manage data to make informed data-driven decisions, make future predictions, Etc.
📉📈 Data analytics is the process of analyzing data and improving problem-solving techniques. 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.
- Numpy:- It supplies a range of functions and tools for working with arrays, matrices, and numerical operations.
- Pandas:- Pandas provide an easy-to-use interface for working with data frames, a two-dimensional data structure similar to a table in a spreadsheet.
- Matplotlib:- Matplotlib is a powerful tool for visualizing data and is widely used in data analytics.
- Visualization:- Visualization is an important aspect of data analytics as it provides a way to understand and communicate data.
- Seaborn:- Seaborn provides a range of functions and tools for creating advanced visualizations, such as heatmaps, box plots, and violin plots, Etc.
Data Visualization and seaborn
📊 Data visualization is the process of converting data into information in the form of Graphs, Charts, Diagrams, Pictures, Images etc, or help decision-making in the mean time.
Data visualization makes it easy to visualize, understand and analyze the data.
📊 Seaborn is a python library that is used to create Plots and graphs. Seaborn also provides a wide range of built-in visualization functions, including scatter plots, line plots, bar plots, histograms, box plots, heat maps, etc. Seaborn functions are designed to work with a variety of different types of data and can be customized using a variety of different parameters. Using the Seaborn library makes work even more accessible, and learning seaborn is much easier than other libraries.
What is tableau and power BI?
👉 Tableau is a powerful data visualization tool that is being used across the world to explore and analyze data through interactive and beautiful charts, graphs, and dashboards. It can handle large, complex datasets and provides advanced analytics capabilities to identify trends and patterns in the data. Tableau also offers features for easy sharing and collaboration on data insights. It provides a drag-and-drop interface for creating visualization effects and supports data from various sources.
👉 Power BI is a business intelligence and data visualization tool created by Microsoft. It allows users to analyze and visualize data, create interactive dashboards and share insights. It can integrate with other Microsoft products. It is also available as a cloud-based service and desktop application.
Database Management
In data science course, databases are used to store and manage large amounts of data. Popular database management systems are as follows.
🗄️ Relational databases (like MySQL, PostgreSQL, and Oracle)
🗄️ NoSQL databases (such as MongoDB, Cassandra, and Neo4j)
🗄️ Apache Hadoop (which includes HDFS for storage
🗄️ Hive for data warehousing and analysis), and Apache Spark (a fast and general-purpose cluster computing system).
The choice of database management system depends on the size and type of data, performance requirements and desired level of control over the data.
Machine Learning and Deep Learning
In a Data science course Artificial Intelligence (AI) focuses on developing computer programs that can perform tasks, like learning, decision-making and problem-solving, that typically require human intelligence.
Machine Learning (ML) is a subfield of AI that deals with training models to automatically improve their performance based on experience. In ML algorithms learn from data by making predictions and updating themselves based on the accuracy of their Predictions.
Deep Learning (DL) is a type of Machine learning that utilizes multi-layered artificial neural networks to learn from data and make predictions. DL is effective in handling complex tasks like image and speech recognition, natural language processing, and decision-making.
Difference between Python and R language
Sr no. | Feature | Python | R |
| 1 | Definition | Python is a powerful programming language that can do magic or logic. Still, all the time, logic is behind the code. It is the most widely used and preferred language among developers because it is simple to learn and works flawlessly. | R is a statistical computing and graphics language and environment developed by R Foundation for Statistical Computing. |
| 2 | Syntax | Print(“pass”) | data(“iris”) # Load the iris dataset print(head(iris, 6)) # Print the first 6 rows of the dataset |
| 3 | Libraries | Python has built-in libraries and functions. | R has a wide range of libraries for statistical analysis, data visualization, and machine learning. |
| 4 | Community | Python has a large and active community of developers who contribute to the development of the language and its libraries. | R has a smaller but passionate community of users and developers who are mainly focused on data analysis and statistical computing. |
| 5 | Visualization | Python has a number of libraries for data visualization, including Matplotlib, Seaborn, and Plotly. | R has built-in visualization capabilities with libraries such as ggplot2 and lattice, which are popular among data scientists and statisticians. |
Conclusion
Data Science is a rapidly growing field with vast potential for solving real world problems and making data-driven decisions. A data science course will be valuable for careers in various industries, including finance, healthcare, marketing, and technology. To be successful in data science, It is important to continuously learn, keep yourself up to date with new technology, and practice problem solving with real world data.
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