Data Science & Machine Learning Internship Program
Build a capstone project & earn Technogeeks Internship Certificate
- Getting insights using python analysis and visualizations on finance credit score data.
- Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.
- Practice project on decision tree and random forest using social network data to predict if someone will purchase an item or not.
- Become a Data Science Expert with 100% hands-on training
- Live instructor-led online classes by industry experts
- Attend the demo to get a certificate of participation
- Last few seats available
- Next demo is on Friday, 29th April at 12:00 PM IST
Free Demo
Attend the free demo for:
- A live instructor-led session about Data Science & Machine Learning
- Current Data Science opportunities
- Internship Program Details:
- Instructor-led program
- Program schedule
- Hands-on projects
- Fee and payment options
- You will get a Certificate of participation after you attend the session!
- Register for the upcoming demo session now:
Program Benefits
Why Data Science?
Build a capstone project & earn an Internship Certificate
- Build a Netflix Like Movie Recommender System
- Master Python, Machine Learning, Deep Learning and Tableau
- Become a Data Science Expert with 100% hands-on training
- Live instructor-led online classes by industry experts
- Attend the demo to get a certificate of participation
Technologies You Will learn
Data Science Internship Program Certificate
GET CERTIFIED ON COURSE COMPLETION
Our Alumni work at
Technogeeks learners have been placed at many companies with a global presence with the help of our Career and Placement Assistance Programs. A few of these companies are:
Data Science Training Syllabus
Best Blended Syllabus for Data Science Course in Pune with Placement Oriented Training Institute
- What is Python and brief history
- Why Python and who use Python
- Discussion on Python 2 and 3
- Unique features of Python
- Discussion on various IDE’s
- Demonstration of practical use cases
- Python use cases using data analysis
- Installing python
- Setting up Python Development Environment
- Installation of Jupyter Notebook
- How to access our course material using Jupyter
- Write your first program in Python
- Deployment on local and cloud platforms using Google Colab
- Introduction to Python objects
- Python built-in functions
- Number objects and operations
- Variable assignment and keywords, String objects and operations
- Print formatting with strings
- List objects and operations
- Tuple objects and operations
- Dictionary objects and operations
- Sets and Boolean
Object and data structures - Assessment test
- Introduction to Python statements
- If, elif and else statements
- Comparison operators
- Chained comparison operators
- What are loops
- For loops
- While loops
- Useful operator
- List comprehensions
- Statement assessment test
- Game challenge
- Methods
- What are various types of functions
- Creating and calling user defined functions
- Function practice exercises
- Lambda Expressions
- Map and filter
- Nested statements and scope
- Args and kwargs in Python
- Functions and methods assignment
Milestone Project using Python
- Process files using python
- Read/write and append file object
- File functions
- File pointer and operations
- Introduction to error handling
- Try, except and finally
- Python standard exceptions
- User defined exceptions
- Unit testing
- File and exceptions assignment
- Python inbuilt modules
- Creating UDM-User defined modules
- Passing command line arguments
- Writing packages
- Define PYTHONPATH
- __name__ and __main__
- Object oriented features
- Implement object oriented programming with Python
- Creating classes and objects
- Creating class attributes
- Creating methods in a class
- Inheritance
- Polymorphism
- Special methods for class
- Collections module
- Datetime
- Python debugger
- Timing your code
- Regular expressions
- StringIO
- Python decorators
- Python generators
- Install packages on python
- Introduction to pip, easy install
- Multithreading
- Multiprocessing
- Understanding Machine Learning
- Scope of ML
- Supervised and Unsupervised learning
- Milestone Project - 2
- Introduction to data analysis
- Why Data analysis?
- Data analysis and Artificial Intelligence Bridge
- Introduction to Data Analysis libraries
- Data analysis introduction assignment challenge
- Introduction to Numpy arrays
- Creating and applying functions
- Numpy Indexing and selection
- Numpy Operations
- Exercise and assignment challenge
- Introduction to Series
- Introduction to DataFrames
- Data manipulation with pandas
- Missing data
- Groupby
- Merging, joining and Concatenating
- Operations
- Data Input and Output
- Pandas in depth coding exercises
- Text data mining and processing
- Data mining applications in Data engineering
- POC - Analysis of e-commerce dataset using pandas
- POC - Getting insights on employee salaries data using data analysis in python
Matplotlib
- Plotting using Matplotlib
- Plotting Numpy arrays
- Plotting using object-oriented approach
- Subplots using matplotlib
- Matplotlib attributes and functions
- Matplotlib exercises
Seaborn Visualization
- Categorical Plot using Seaborn
- Distributional plots using Seaborn
- Matrix plots
- Grids
- Seaborn exercises
Project- Getting insights using python analysis and visualizations on finance credit score data.
Assignment - Pandas built-in data visualization Data visualization
- Comparison Between Tableau & Programming Based
Data Visualization - Need Of Tableau
- Types Of Data Sources Supported By Tableau For Report Development
- How To Build Report & Dashboard in Tableau
- How To Build Charts In Tableau
- Data Visualization Using Tableau Features
- Need of Mathematics for Data Science
- Exploratory data analysis (EDA)
- Numeric Variables
- Qualitative and Quantitative Analysis
- Types of Data Formats
- Measuring the Central Tendency - The Model
- Measuring Spread - Variance and Standard Deviation
- Euclidean Distance
- Confidence Coefficient
- Understanding Parametric Tests
- Introduction to Data Science
- Introduction to Artificial Intelligence
- Introduction to Machine Learning
- Need of Machine learning in forecasting
- Demand of forecasting analytics in current industrial trends
- Introduction to Machine Learning Algorithms Categories
- Introduction to Natural Language Processing (NLP)
- Introduction to Deep Learning
Linear Regression with Python
- Introduction to Regression
- Exercise on Linear Regression using Scikit Learn Library
- Project on Linear regression using USA_HOUSING data
- Evaluation of Linear regression using python visualizations
- Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.
K- Nearest neighbours using Python
- Exercise on K-Nearest neighbors using Sci-kit Learn Library
- Project on Logistic regression using Dogs and horses’ dataset
- Getting the correct number of clusters
- Evaluation of model using confusion matrix and classification report
- Standard scaling problem
- Practice project on KNN algorithm.
Decision tree and Random forest with python
- Intuition behind Decision trees
- Implementation of decision tree using a real time dataset
- Ensemble learning
- Decision tree and random forest for regression
- Decision tree and random forest for classification
- Evaluation of the decision tree and random forest using different methods
- Practice project on decision tree and random forest using social network
- Data to predict if someone will purchase an item or not
Support Vector Machines
- Linearly separable data
- Non-linearly separable data
- SVM project with telecom dataset to predict the users portability
Principal Component Analysis
- Introduction to PCA
- Need for PCA
- Implementation to select a model on breast-cancer dataset
- Model evaluation
- Bias variance trade-off
- Accuracy paradox
- CAP curve and analysis
Clustering in unsupervised learning
- K-means clustering intuition
- Implementation of K-means with Python using mall customers data to implement clusters on the basis of spending and income
- Hierarchical clustering intuition
- Implementation of Hierarchical clustering with python
Association Algorithms
- A priori theory and explanation
- Market basket analysis
- Implementation of Apriori
- Evaluation of association learning
POC - To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.
- Introduction to Natural Language processing
- NLTK Python library
- Data stemming technique
- Data Vectorization
- Exercise on NLTK
- POC- Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention.
- Neural Network and Deep Learning
- What is TensorFlow?
- TensorFlow Installation
- TensorFlow basics
- TensorFlow with Contrib Learn
- TensorFlow Exercise
- What is Keras?
- Keras Basics
- Pipeline implementation using Keras
- MNIST implementation with Keras
- REST principles
- Creating application endpoints
- Implementing endpoints
- Using Postman for API testing
- CRUD operations on database
- REST principles and connectivity to databases
- Creating a web development API for login registers and connecting it to the database
- Deploying the API on a local server
- Project use cases Introduction
- Project Scenarios
- Project life cycle
- What is version controlling in project management
- What is GitHub
- Significance of GitHub in project management
- Code submission for testing and deployment
- Predictive analytics tools and techniques
- Project best practices
Admission Process
FAQ
In the 21st century, the new oil is "Data", and "Data Science" is the data refinery to get the insights from RAW data!
As internet use became widespread with it data generation is exploded and obviously, there was the need to understand the data to use it for data-driven decision making.
So there is a need for highly skilled "data science" professionals to get business insights before competitors cache up with your business.
Calculus, linear algebra, and statistics are three math disciplines that will regularly show up if you ask any data science professional for the data science prerequisite.
The good news is that statistics is the only math that you need to master for most data science profiles
The core data science skills that an aspiring data science professional needs to add to their skills resume are:
Python, Machine Learning, Statistics, Data Visualisation, Scikit-learn, R, Business understanding, communication, Math, SQL, Critical Thinking, ETL - preparation, and Excel.
Hot/Emerging DS Skills you should look for:
Deep Learning, TensorFlow, Apache Spark, NLP - Text Processing, Pytorch, Bigdata tools, Unstructured data, No-SQL, Hadoop, Kaggle & Scala
Like any other field, data science is always evolving with technology innovation and market maturity. According to experts, getting data ready for analysis takes up to 80% of a data scientist's time. But as automation progressing in data science it becoming like web designing where you don't need to write code but the tool will do that for you.
As quantum computing & quantum information science are advancing data scientists must understand quantum mechanics and how to use a quantum algorithm to solve a particular problem.
PyCharm is undoubtedly the most well-known Python IDE. It has an excellent debugger, and works smoothly with git, and works easily with the multiple Python versions with the virtual environment. Reindexing is relatively fast with an initiative interface. The community version is free and has all essential features
You will do your Assignments/Case Studies using Jupyter Notebook and Pycharm that
will be installed on your system and access details will be shared during the class. For any doubt, the Technogeeks support team will promptly assist you. Also, if in case you have any system configuration issues, don't worry we have Google Collab as solution available.
