Data Science Master Course

 Learn Data Science with Python

Online Instructor-Led Data Science using Python Training

39

Modules

9+

Hands-on Projects

10+

Industry Experts

108+

Live Class Hours

50+

Placement Partners

72+

Assignments Hours

Students

Exclusive Offers!

100%

Interview Guarantee

27%

Discount

The Journey of a Data Scientist

Why Study Python for Data Science?

This Python for Data Science Course program helps to create a strong foundation for Data Scientists to enter the challenging field of AI.

Data Science Using Python Specialization consists of Instructor-Led Online courses and a number of Self-Paced Foundation courses. It is thoughtfully designed to allow learners with a programming background to make a transition into the analytics industry with the required skill-set, using Python programming language.

Post completion of this Python Data Science Course program, learners will be prepared to devise solutions for real-time problems in the industry.

The course constitutes of an assignment for every topic, and multiple options of Capstone Project from different domains.

Who is this Data Science Course for? 

  • Freshers aspiring an exemplary career in Data Science 
  • Non-IT Professionals desirous of making a career shift to the Data Science Industry 

Our Data Science Using Python Course Enrollments

  • Students 60% 60%
  • Non-IT Professionals 40% 40%
  • IT Professionals 30% 30%

36 Modules

Our Python for Data Science Course Syllabus 

  • 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
  • Analytics
  • Business Analytics
  • Business Intelligence
  • Industry Examples
  • What is Visualization
  • Importance of data visualization.
  • Data Visual Analytic pipeline
  • Intro to DV tools that are ruling the industry
    • Tableau
    • Power BI
    • Qlik Vie

  • Introduction

 

    • GUI
    • Cell Referencing
    • Freeze Panes
    • Sum Function

  • Useful Functions

 

    • Counting functions
    • Summing functions
    • Averaging functions

  • Sorting & Filtering

 

    • Multi-level Sort
    • Custom Filter

  • Duplicates

 

    • Remove duplicates

  • Working with text

 

    • Concatenate
    • Left, right, upper, lower

  • Working with conditions

 

    • Conditional Formatting
    • Logical Operations 
    • IF, AND, OR, Nested IF

  • Data functions

 

    • Splitting
    • Creating dates

  • Fetching and comparing data sets

 

    • $ Referencing
    • Vlookup and Hlookup
    • Match and Index

  • Named ranges

 

    • Name Box

  • What-if-Analysis

 

    • Goal Seek
    • Data Table
    • Scenario Manager

  • Solver Plug-in

 

    • Solver plug-in

  • Pivot tables

 

    • Dimensions & Measures
    • Multi-layer Pivot Table
    • Summarize Values by
    • Show Values as Grouping

  • Charts

 

    • Various Charts
    • Pivot Charts
    • Combo Charts
    • Sparklines
  • Descriptive Statistics
  • Inferential Statistics
  • Regression
  • Anova

  • Core Database Concepts

 

    • Relational Database Concepts
    • Data types
    • Tables

  • Queries

 

    • Select, Insert, Update, Delete

  • Views

 

    • Creating and Updating
  • Installation
  • Python – Syntax
  • Python – Variables and Datatypes
  • Python – Numbers
  • Strings
  • Sequences
  • List
  • Tuples
  • Ranges
  • Dictionary
  • Sets
  • Operators
  • If..Else.. Statements
  • For Loop
  • While Loop
  • Break
  • Continue
  • Pass
  • Date & Time
  • Functions
  • Packages and modules
  • Reading a File
  • Writing into File
  • Class & Objects
  • Python – Exceptions
  • Regular Exp
  • Mathematics
  • Environment Setup
  • Database Connection
  • Creating a New Database
  • Creating Tables
  • Insert Operation
  • Read Operation
  • Update Operation
  • Join Operation
  • Performing Transactions
  • ndarray
  • Array Creation
  • Data Type Objects
  • Data type Object (dtype) in NumPy
  • Indexing
  • 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
  • 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
  • Reflection
  • Barrier objects
  • Timer objects
  • Garbage collectio
  • 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)
  • Graphically Displaying Single Variable
  • Measures of Location
  • Measures of Spread
  • Displaying relationship – Bivariate Data
  • Scatterplot
  • Measures of association of two or more variables
  • Covariance and Correlation
  • Probability
  • 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
  • Lemmatization
  • Stemming
  • 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
  • N-Grams
  • 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

6 Courses

Data Science Courses

  • Python Programming Foundation (10 Chapters) – Self Study Course
  • Statistics Foundation (17 Chapters) – Self Study Course
  • SQL Foundation (6 Chapters) – Self Study Course
  • Data Science using Python (18 Sessions) – Instructor-Led Online Course
  • Data Science using R (15 Chapters) – Self Study Course
  • Introduction to Tableau (3 Sessions) – Instructor-led Online Course

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 Analytics Techniques & Fundamentals of Excel

Gettig started with basic functions regarding Sum, Multiplication, Subtraction, Average etc

Working on Sorting & Filtering

Work on SUMIF, COUNTIF etc. formula, Sort & Filtering, Type of Data in Excel, Useful Functions, Sorting & Filtering, Data Validation & Data Cleaning

Working with Dates & Starting out with Other Conditions

Date Functions and working with Conditions in Excel

Getting Started with Data Manipulation

Data Manipulation using V-Lookup & H-Lookup, Database function

Working with Tables & Drop-down Functions

Match & Index Formula, Dropdown function, Excel Tables & Data Analysis

Getting Deep with Analytical Applications

Protection of sheet, Converting data range into the table, Use of Group, Ungroup & Subtotal Function, What-if-analysis Data Visualization, Array Functions & Introduction to VBA Macros

Working with Pivot Tables

Get started with working on Pivot Tables

Getting Friendly with the Basics of SQL

Selection of data from the given table, Renaming of Column, Selection and Filtering of data

Working with Intermediate SQL Capabilities

Creating Distinct list, Joining of two different tables using key, Creating of tables, Display of tables, Updation of data.

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

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
Download curriculum (.pdf)
Get compelte details of this course

4 Projects

Python for Data Science 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.

 

Risk Assessment of Credit Card Customers

data analyst course

As a Data Analyst, you will get a deep insight into the banking and finance industry. You will be asked to work on analysing the data of credit card customers and identify the risks associated with each of them.

After analysis, you will be expected to determine which kind of customers are risky, and which ones are safe. You will also need to find out which one of those should be blacklisted.

IPL Data Analysis

data analyst course

As a Data Analyst, this Capstone project will help you analyze IPL data from previous years. You will be able to identify how each player has been performing as a Batsman, Bowler, and a Fielder. You will also understand how each Team has been performing.

Your skills of Excel will be put to test with the kind of analysis that you have to solve.

 

Analyzing the Business of Computer Rentals

data analyst course

Corporations, Small Business and Freelancers are constantly in need of hardware for short spans of time to do certain gigs. Reasons may range from travelling to working on a specific project. 

In all rationality, it’s a lot more expensive to buy such equipment than taking it on rent. In this project, you will be expected to draw inferences from data related to computer rentals and relevant recommendations. 

Natural Language Processing

Duration: 3 Weeks

Project Description:

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.

Key Takeaway:

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:

  1. Remove stop words
  2. Apply Stemming and Lemmatization
  3. Create a cluster of words
  4. Build a sentiment analysis model and a clustering model

Healthcare Analysis

Duration: 3 Weeks

Project Description:

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?

Key Takeaway:

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:

  1. Parse and store in an easily understandable and readable form
  2. Exploratory data analysis to better understand the data
  3. Using Statistical concepts like Hypothetical testing
  4. Identify features to predict whether a subject is alcoholic or not
  5. Use machine learning algorithms to develop a suitable classifier

Bank Marketing

Duration: 3 Weeks

Project Description:

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.

Key Takeaway:

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:

  1. Build the model to predict if a customer will subscribe
  2. Identify influential factors to form marketing strategies
  3. Improve long-term relationship with the clients

Deep Learning Based Project

Duration: 3 Weeks | Price: ₹5000 (Including Tax)

Project Description:

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.

Key Takeaway:

This project focuses on the implementation of Neural Networks to solve complex unstructured data problems. The objective is to:

  1. Build the model to classify the various categories (analytic vertical) of clothing/fashion related images.
  2. Understanding the implementation of deep learning concepts through Tensorflow and Keras.
  3. Model optimization by tuning hyper-parameters and implementing dropout layers.

For further information on Python for Data Science Capstone Projects

Schedule of Data Science With Python Online Training 

 

Live Classes

3 hours per week

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Duration

39 Weeks

j

Assignments

72+ hours

 

Upcoming Data Science Using Python Course Batches Start on

 

 

Oct 20th, 2019 (Sunday)

10 AM -1:30 PM (IST)

If you do not like the Python for Data Science training, take 100% refund within 15 days!

Qualifications for our Interview Guarantee Offer

  • BE/B.Tech Computer Science/IT/ MCA / MSc. IT / MA. Statistics / MA. Mathematics
  • CGPA of 6.0 & above (50% & above in MCA / MSc IT)
  • 60% of Marks in 10th and 12th Exams
  • Immediately Available to Join the Organization
  • Successful Completion of the Course along with Aptitude & Coding Tests

Note: Students from Non-Metro/Non-IT cities need to be self-located in the hiring locations during the placment process.

6 Tools

Data Science Programming Languages, Platforms and Tools

You will become comfortable with Python Data SCience tools that are widely used in the industry by Data Scientists.

Language: Python

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.

Excel

PowerBI

MYSQL

Tool: Tableau

Tableau is the analytics platform that disrupted the world of business intelligence. Learners will be introduced to this tool.

Know everything that we teach in our Data Science with Python Course

100+ Hrs of Instructor-led Online Classes

Why Learn Online?

Online learning allows you to learn Python for Data Science with experts around the Globe.

Experience learning online yourself through a one-to-one Live Demo Session!

We take 100s of demo sessions every day!

Best Trainers

Learn directly from Global experts with  Experience from different industries and domains.

Attend from Anywhere

You can attend live sessions from anywhere in the world including on Mobile.

No Need to Travel

Save upto 120 hours of precious time, which would otherwise get wasted in travel.

Interactive & Practical

100+ hours of assignments ensure that you are learning hands-on.

Class Recordings

Even if you miss a live session, you can learn through the recording, which stays with you forever.

Lifetime Updates

Technology is dynamic. You will have access to the revised content for the lifetime.

Industry Experts as Trainers

Python for Data Science Course Trainers

Get trained by the Data Scientists with global experience and expertise across different industries.
100s of Students Attend our Data Science Using Python Demo Classes Every Week 

50+ Placement Partners

100% Interview Guarantee Offer

Digital Vidya offers 100% Interview Guaranteed support to freshers and eligible new graduates who get certified through the 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.

Resume Creation

On successful completion of the course including assignments & certifications, we work with the candidate to create an effective resume.

01

Job Application

The updated Resume is then shared with relevant organisations. On shortlisting, we follow-up with an initial round of discussion.

02

Interview Readiness

Based on the organizations & the profiles for which the candidate is shortlisted, we help the candidate prepare for the complete interview process.

03

Selection & Joining

After a successful interview, we guide the candidate from accepting the offer to joining the organization for a successful career.

04

For further information on Placement Support on our Data Science Using Python Course 

2 Certifications

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 Certificate

How to get this Certificate?

On successful passing of the VSkills examination, the participant will get a Certificate issued by VSkills.

For further information on Python Data Science Certifications

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.

Mohan Kumar

Senior Software Engineer

Great experience. Easy and organized learning, great approach.

Prerna Sathiyal

Student

Digital Vidya gave me a comprehensive knowledge of Data science within a very short period of time.

Rahul God

Founder COO

Case studies and projects improved my skills and given me confidence to call myself a data scientist.

Arvind S

Academician

This course is best for beginners and it will give you complete exposure of every field of Data Science and Machine Learning.

Anshul Singh

Student

It was a great experience. Got to learn many new things going on in the present industry.

Lipi Sahu

Student

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

General

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 will I do if I miss my Instructor-Led Online class?

You will be getting access to the recording of every class for your own revision and practice. In case you miss a class, it is important that you go through the topics using the  recording prior to the next class, so that you can follow through the next session effectively. Also, you will be required to submit the assignment on time even for the missed class.

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.

What is the placement support after this course?

Digital Vidya provides placement support to students/freshers/young graduates under 2 years of experience as part of a qualified offering. The experienced profiles get limited level of assistance, as the experienced students come in all variety and may not always match the job requirements. The experienced professionals, It is highly recommended that one looks for opportunity within their organisation to establish their new skills prior to changing company.

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 happens if I refuse the placement offer?

Once you refuse the offer, you may have to wait your turn for the next opportunity. We will not promise you the next interview.

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.

w

Discuss with a Career Advisor

Not sure, what to learn and how it will help you?

Attend a Demo Session

Not sure about online classes?

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Application

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