Data Science Course (Self Paced)
Learn Advanced Data Science & Machine Learning using Python
The Digital Vidya Advantage
Get Certified by NASSCOM FutureSkills
100% Interview Guarantee on Completion
Curriculum approved for 2800 companies requirements
Why Study Data Science?
*AI Professionals employability gaps in Indian IT industry as per NASSCOM Report
*AI Professionals employability gaps in Indian IT industry as per NASSCOM Report
Are you Ready to Learn Data Science?
Whether you are a student or an experienced professional, if you can fulfil following pre-requisites, you can be a Data Scientist
- Programers or non-programmers with a desire to learn Programming
- Good analytical skills
- Passionate problem solver
- Willing to spend 20 hrs/week for 3-4 months on training
Profiles of Digital Vidya’s Students
- Students 60% 60%
- Non-IT Professionals 40% 40%
- IT Professionals 30% 30%
Discuss the course relevance for you
NASSCOM FutureSkills Certified Course
Get Trained & Certified for Data Science Jobs in 2800 Companies
- NASSCOM FutureSkills has certified Digital Vidya’s Data Science Course as it covers 100% of the Data Science skills across Industries
- NASSCOM Data Science curriculum is based on skills required by 2800+ NASSCOM member companies
- Every course participant will get NASSCOM FutureSkills Certificate on successful course completion
20+ leading companies were involved in defining NASSCOM Course Curriculum
50+ Placement Partners
100% Interview Guarantee Offer
Digital Vidya offers 100% Interview Guaranteed support to eligible new graduates and working professionals, who get certified through Python for Data Science Course.
We have a dedicated placement cell, which works closely with our participants for their placement needs. Here is a snapshot of our placement process.
On successful completion of the course including assignments & certifications, we work with the candidate to create an effective resume.
The updated Resume is then shared with relevant organisations. On shortlisting, we follow-up with an initial round of discussion.
Based on the organizations & the profiles for which the candidate is shortlisted, we help the candidate prepare for the complete interview process.
Selection & Joining
After a successful interview, we guide the candidate from accepting the offer to joining the organization for a successful career.
For further information on Placement Support on our Data Science Using Python Course
Python Basic, Data Science, Data Visualization & Machine Learning using Python
Python for Data Science Course Syllabus
- Introduction to Data Science & Analytics Techniques
- Python Fundamentals
- Python MySQL
- Pandas DataFrame & Data Analysis
- Statistics Fundamentals
- Machine Learning with Python
- Regression & Classification
- Support Vector Machine Introduction
- Machine Learning - Clustering Introduction
- Recommender Systems
- Introduction to NLP
- Introduction to Data Science
- Introduction to Python
- Data Analysis Pipeline
- What is Data Extraction
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Overview of the Analytics Techniques
- Business Analytics
- Business Intelligence
- Industry Examples
- Python – Syntax
- Python – Variables and Datatypes
- Python – Numbers
- If..Else.. Statements
- For Loop
- While Loop
- Date & Time
- Packages and modules
- Reading a File
- Writing into File
- Class & Objects
- Python – Exceptions
- Regular Exp
Functions in Python
- class method vs static method in Python
- Write an empty function in Python – pass statement
- Yield instead of Return
- Return Multiple Values
- Partial Functions in Python
- First Class functions in Python
- Precision Handling
- *args and **kwargs
- Python closures
- Function Decorators
- Decorators in Python
- Decorators with parameters in Python
- Memoization using decorators in Python
- Python bit functions on int (bit_length, to_bytes and from_bytes)
Object -Oriented Concepts
- Class, Object and Members
- Data Hiding and Object Printing
- Inheritance, examples of an object, subclass and super
- Polymorphism in Python
- Class and static variable in Python
- Class method and static method in Python
- Changing class members
- Constructors in Python
- Destructors in Python
- First-class function
- str() vs repr()
- str() vs vpr()
- Metaprogramming with metaclasses
- Class and instance attribute
- Barrier objects
- Timer objects
- Garbage collection
- Environment Setup
- Database Connection
- Creating a New Database
- Creating Tables
- Insert Operation
- Read Operation
- Update Operation
- Join Operation
- Performing Transactions
- Array Creation
- Data Type Objects
- Data type Object (dtype) in NumPy
- Basic Slicing and ALinear Algebra
- Sorting, Searching and Counting
- Set 1 (Introduction)dvanced Indexing
- Iterating Over Array
- Binary Operations
- Mathematical Function
- String Operations
- Set 2 (Advanced)
- Multiplication of two Matrices in Single line using Numpy in Python
- Creating a Pandas DataFrame
- Dealing with Rows and Columns in Pandas DataFrame
- Indexing and Selecting Data with Pandas
- Boolean Indexing in Pandas
- Conversion Functions in Pandas DataFrame
- Iterating over rows and columns in Pandas DataFrame
- Working with Missing Data in Pandas
- Working With Text Data
- Working with Dates and Times
- Merging, Joining and Concatenating
- Data visualization using Bokeh
- Exploratory Data Analysis in Python
- Data visualization with different Charts in Python
- Data Analysis and Visualization with Python
- Math operations for Data analysis
- Graphically Displaying Single Variable
- Measures of Location
- Measures of Spread
- Displaying relationship – Bivariate Data
- Measures of association of two or more variables
- Covariance and Correlation
- Joint Probability and independent events
- Conditional probability
- Bayes’ Theorem
- Prior, Likelihood and Posterior
- Discrete Random Variable
- Probability Distribution of Discrete Random Variable
- Binomial Distribution
- Continuous Random Variables
- Probability Distribution Function
- Uniform Distribution
- Normal Distribution
- Point Estimation
- Interval Estimation
- Hypothesis Testing
- Testing a one-sided Hypothesis
- Testing a two-sided Hypothesis
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
- Regression – Features and Labels
- Regression – Training and Testing
- Regression – Forecasting and Predicting
- Regression – Theory and how it works
- Regression – How to program the Best Fit Slope
- Regression – How to program the Best Fit Line
- Regression – R Squared and Coefficient of Determination Theory
- Model evaluation methods
- Classification Intro
- Applying K Nearest Neighbors to Data
- Euclidean Distance theory
- Decision Trees
- Regression Trees
- Random Forests
- Boosting Algorithm
- Principal Component Analysis
- Linear Discriminant Analysis
- Vector Basics
- Support Vector Machine Fundamentals
- Constraint Optimization with Support Vector Machine
- Beginning SVM from Scratch in Python
- Support Vector Machine Optimization in Python
- Visualization and Predicting with our Custom SVM
- Kernels Introduction
- Soft Margin Support Vector Machine
- Handling Non-Numerical Data for Machine Learning
- K-Means with Titanic Dataset
- K-Means from Scratch in Python
- Finishing K-Means from Scratch in Python
- Hierarchical Clustering with Mean Shift Introduction
- Introduction Naive Bayes Classifier
- Naive Bayes Classifier with Scikit
- Introduction into Text Classification using Naive Bayes
- Python Implementation of Text Classification
- Content-based recommender systems
- Collaborative Filtering
- Text Preprocessing
- Noise Removal
- Lexicon Normalization
- Object Standardization
- Text to Features (Feature Engineering on text data)
- Syntactical Parsing
- Dependency Grammar
- Part of Speech Tagging
- Entity Parsing
- Phrase Detection
- Named Entity Recognition
- Topic Modelling
- Statistical features
- TF – IDF
- Frequency / Density Features
- Readability Features
- Word Embeddings
- Important tasks of NLP
- Text Classification
- Text Matching
- Levenshtein Distance
- Phonetic Matching
- Flexible String Matching
- Important NLP libraries
Python Basics, Data Science, Data Visualisation & Machine Learning
Data Science Course Curriculum
72+ Hours of Hands on Assignments
Hands-on Python for Data Science Course Assignments
Modules Assignments to strengthen Your Data Science Skills Using Python
Note: The following list is not comprehensive. We add/edit assignments based on feedback from both industry experts and participants.
Introduction to Python
The variables, data types and all kinds of operators (arithmetic, logical, comparison) will be introduced in this assignment.
Dive Deep into Python
The important data types of Python List, Dictionary etc will be covered in addition to conditional and iterative loops. Some aspects of data cleansing will also be covered.
Introduction to NumPy Library
NumPy is an important Python library for numerical/statistical operations and is of utmost importance in Data Science. Here you will be introduced to the Numpy Library.
Data Manipulation using Pandas Library - I
Data manipulation is critical to Data Science. This manipulation of data is done using Pandas library in Python. You will be introduced to Pandas, functions (iloc, tail, head, groupby, fillna, etc) which are most commonly used will be discussed here.
Data Manipulation using Pandas Library - II
This is the 2nd part of data manipulation using Pandas. You will be made to solve complex questions for manipulating data. You will build functions and apply those on the Pandas data frames which is practical and applicable.
Analyzing & Manipulating Data
Time series is a kind of data which is often found for analysis. Analyzing, manipulating and making sense out of this data is an outcome of this assignment.
Data visualization is an important part of Data Science. As we create stories/insights from the data, these stories/insights are shown through visualizations. This assignment will help to implement various visualizations using Matplotlib and Seaborn Python libraries.
Merge Multiple Datasets into One
Data comes from disparate sources and it is a common use case to join/merge/concatenate multiple datasets into one. This assignment will help you learn to merge/concatenate datasets using Python and also make your work on melting/changing dimensions of datasets by converting rows to columns and vice versa
Statistics: Probability, Hypothesis Testing
Multiple Linear Regression & Quadratic Regression Analysis
Introduction to Trees, Decision Trees, Ensemble Learning (Random Forest)
Classification Introduction, Logistics Regression & Text Analysis Using Classification Algorithms
Unsupervised Learning, Unsupervised Learning Techniques-K Means Clustering, Hierarchical Clustering
Bias-Variance Trade-off, Model Evaluation Techniques
Logistic Regression Model Tuning
Python for Data Science Course Capstone Projects
Every participant is mandated to solve one Capstone Project to be eligible for our Python for Data Science Certification. The learner is encouraged to solve all available projects to sharpen the skills across several domains.
Natural Language Processing
Duration: 3 Weeks
This is one of the most applied areas for AI, Data Science, and Machine Learning across domains and industries. The real world is filled with mostly messy text data, and handling text is an important step towards making smarter algorithms. Using IMDB dataset from the movie domain, the learner will apply the most common concepts of NLP.
This project will empower the learners to build intermediate skills in the natural language processing domain. A few of the fundamentals of working with textual data covered in this project are:
- Remove stop words
- Apply Stemming and Lemmatization
- Create a cluster of words
- Build a sentiment analysis model and a clustering model
Duration: 3 Weeks
Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. For this project, we will use the large EEG database at UCI Machine learning repository. This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. One fascinating question is whether the patterns are different for an alcoholic and regular subject?
This capstone project focuses on EEG data analysis, giving an opportunity for students to learn through complexities in dealing with such complex real-world data. The project contains the following exercises:
- Parse and store in an easily understandable and readable form
- Exploratory data analysis to better understand the data
- Using Statistical concepts like Hypothetical testing
- Identify features to predict whether a subject is alcoholic or not
- Use machine learning algorithms to develop a suitable classifier
Duration: 3 Weeks
The banking industry is working in a very competitive environment and needs to strategize to grow its business. This project is related to the marketing campaigns related to term deposits, making an interesting multi-disciplinary work that mixes both the finance and the marketing domain.
The approach to this project is to think, define, design, code, test and tune your solution, in such a way that you apply all aspects of the data science process. The data is a real-world data with unclean and null values.
The objective is to:
- Build the model to predict if a customer will subscribe
- Identify influential factors to form marketing strategies
- Improve long-term relationship with the clients
Deep Learning Based Project
Duration: 3 Weeks | Price: ₹5000 (Including Tax)
E-Commerce has experienced considerable growth since the dawn of the internet as a commercial enterprise. Deep Learning excels at identifying patterns in unstructured data and can predict the class of an uploaded image applied on eCommerce context. This project is an attempt to replicate virtual store assistance through image recognition over an eCommerce Fashion MNIST dataset.
This project focuses on the implementation of Neural Networks to solve complex unstructured data problems. The objective is to:
- Build the model to classify the various categories (analytic vertical) of clothing/fashion related images.
- Understanding the implementation of deep learning concepts through Tensorflow and Keras.
- Model optimization by tuning hyper-parameters and implementing dropout layers.
Schedule of Data Science Course (Self Paced) With Python Online Training
6 hours / week
Langauge & Tools
Data Science Programming Languages and Tools in this Course
You will become comfortable with Python Data Science tools that are widely used in the industry by Data Scientists.
Python is becoming the first choice for Data Scientists. The learners will be learning to use all the relevant libraries, NumPy, Pandas, scikit-learn, Matplotlib.
Tool: Jupyter Notebook
An open-source web application that contains live code, visualizations and narrative text. Learners will be using this for all their data science work.
Data Science with Python Course Certifications
Data Science using Python Certificate
How to get this Certificate?
On successful completion of all assignments and 1 Capstone Project, the participant will get a Certificate issued by Digital Vidya.
Data Science NASSCOM FutureSkills Certificate
How to get this Certificate?
On successful passing of the Course, the participant will get a Certificate issued by NASSCOM FutureSkills
Industry Experts as Trainers
Python for Data Science Course Trainers
Get trained by the Data Scientists with global experience and expertise across different industries.
What do our Students say About us?
It was a tremendous journey right from the beginning. Huge opportunity opened in front of us in Data Science world.
Great experience. Easy and organized learning, great approach.
Digital Vidya gave me a comprehensive knowledge of Data science within a very short period of time.
Case studies and projects improved my skills and given me confidence to call myself a data scientist.
This course is best for beginners and it will give you complete exposure of every field of Data Science and Machine Learning.
It was a great experience. Got to learn many new things going on in the present industry.
What do the Industry Leaders say About us?
Digital Vidya is doing a great job at bringing data analytics to the rest of the world!
Akshay Sehgal, General Manager
Digital Vidya is doing a great job of bringing people from diverse set of experiences to one platform for creating the best of Data Science skill pipeline.
Ambuj Kathuria, Head – Data & Analytics
Creating a talent pool in India with Practical hands-on experience in Analytics and Data Science is the need of the hour. Platforms like Digital Vidya are critical to filling this gap.
Ravi Vijayaraghavan, President and Head – Analytics and Decision Sciences
Data Science with Python Course FAQs
Who is the Python Data Science Specialisation course for?
The Python Data Science course is thoughtfully designed to allow learners with programming background to make a
transition into the analytics industry with the correct skill-sets.
We recommend this course to the following:
- Students BE/BTech/MCS/MCA who aspire to make a career into the growing field of Data Science/AI
- Software professionals who aspire to explore a career in the field of Data Science
- IT professionals with a passion for Statistics, Problem-solving, Machine Learning
- Analytics professionals who want to solve deep problems using a programming language
What should you expect after completing Python Data Science Specialisation Course?
This is a comprehensive course that will help you gain an in-depth understanding of end to end data science. You’ll build the foundation of statistics, SQL, Exploratory Data Science, Machine Learning, Visualisation using both Python and Tableau. You will also have the option of learning R whenever you come across a project that is using R in your career.
BE/B Tech/MCA/MCS Students/Fresh Graduates
What is the starting package in the industry for freshers?
Salaries offered to the freshers vary depending upon factors like premium institutes on-campus versus off-campus. Also, many companies take in interns at and then offer them employment based on performance. Later is most prevalent for off-campus hiring.
Are there more Data Science jobs in small and start-up companies?
Start-up companies are more flexible to hire freshers as they are in the growth phase and they are your optimal chance to get an immediate job and make an entry in the new field. The best part of working with startups is that you will have the opportunity to be the big fish, contributing in building core pieces the work. You should always grab the first job that you get in the new field, gain valuable experience in the first company. The key factor is how much can you learn and contribute in your early years, rather than the size of the company, big or small.
What kind of roles do companies have for freshers in the industry?
You are taken as a trainee – Junior Data Analyst, Data Scientist. You may get a different role and technology based on the requirements and your performance in the selection process.
Will I be able to choose the location, salary and domain for the companies?
The more flexible you are, you increase your chances of getting started with your career.
Can I get refund if I am unable to complete my assignments/project?
You will not be eligible for any refund in case of non-completion of the course.