Data Science Master Course

 Learn Data Science with Python

Online Instructor-Led Data Science using Python Training




Capstone Projects


Industry Experts


Live Class Hours


Placement Partners


Assignments Hours


Exclusive Offers!


Interview Guaranteed



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.

Our Data Science Using Python Course Enrollments
  • Students 60% 60%
  • Working Professionals 40% 40%

6 Courses

Our Curicullum of Python for Data Science Course

Introduction to Data Science

  • Getting started with Jupyter Notebook
  • Introduction to the Open Data Science learning and competitive platforms

Python Programming

  • Introduction
  • Operators
  • Data Types
  • Loops: while & for
  • Conditionals: if-else
  • Functions: defining functions, anonymous functions

Scientific computing with Python – Numerical Python (NumPy)

  • Array Creation
  • Data Types
  • Shape Manipulation
  • Array Indexing
  • Broadcasting
  • Universal Functions
  • Statistical Methods

Introduction to Pandas

  • Data Analysis workflow in Python using Pandas Data Structures
  • Indexing and selecting data
  • Statistical Operations
  • Applying Functions
  • Groupby: split-apply-combine
  • Handling missing data
  • Merging multiple datasets

Data Visualization

  • Simple & multi-line plots, Multiple figures Simple plot with X and Y axis
  • Linestyles and color
  • Mutiple lines on same plot
  • Controlling line properties
  • Adding Lables, gridlines, annotations
  • X and Y ticks and rotations
  • Splines
  • Legends
  • Working with Multiple figures and axes
  • Share X and Y axis
  • Adding subplots

Matplotlib and Seaborn

  • Line Graphs
  • Bar plots
  • Histograms
  • Box plot
  • Stacked plots
  • Scatter plot
  • Pie Chart

Advanced Data Analysis using Pandas

  • Reshaping: stack, unstack, pivot, melt
  • Working with Text data
  • Time series analysis
  • Encoding
  • Handling C Parse Error
  • Data Loading and file formats
  • Loading JSON files
  • XML and HTML web scraping
  • Interacting with HTML and web APIs
  • Interacting with Databases


  • Normal Distribution
  • Hypothesis testing
  • Introduction to z-test and t-test
  • Introduction to Chi-Square distribution

Machine Learning

Simple Linear Regression
  • Hypothesis testing in Linear regression
  • Interpreting slope and intercept coefficients
  • Cost Function in Linear regression
  • Residuals analysis
  • Interpreting R-square
  • Dummy variables encoding
Multiple Linear regression
  • Multicollinearity issue
  • Interpreting Adjusted R-square
  • Outlier detection and treatment
  • Missing values treatment
Decision Trees
  • Gini Index
  • Entropy concept
  • Classification mechanism
  • Issues – Overfitting
  • bias-variance trade-off
  • Different types of decision trees
  • Bagging and Boosting concept
  • Random Forest
  • Introduction to boosting algorithms
Logistic Regression
  • Introduction
  • Sigmoid function
  • Logistic regression Model Evaluation Evaluation Metrics
  • Scoring Confusion Matrix Gain
  • Lift Chart
  • Concordant – Discordant Ratio
Text Mining


  • K Means Clustering
  • Elbow method
  • Hierarchical clustering
  • Kolmogorov Smirnov Chart
  • AUC – ROC Curve

Introduction to BI

  • Connecting to Data
  • Getting Started with Data
  • Managing Extracts
  • Saving and Publishing Data Sources
  • Data Prep with Text and Excel Files
  • Join Types with Union
  • Cross-database Joins
  • Data Blending

Visual Analytics

  • Drill Down and Hierarchies
  • Sorting
  • Grouping
  • Additional Ways to Group
  • Creating Sets
  • Working with Sets
  • Parameters
  • Formatting
  • The Formatting Pane
  • Tooltips
  • Trend Lines
  • Reference Lines
  • Forecasting
  • Clustering


  • Getting Started with Calculations

Dashboards and Stories

  • Getting Started with Dashboards and Stories
  • Building a Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Story Points

Python – Starting your journey in Programming

  • Install Jupyter Notebook
  • Basic Data type and Variables
  • Basic Math Operators
  • Comparison Operators

String Manipulations

  • String datatype
  • String operations

Compound DataTypes

  • Set
  • Tuples
  • Lists
  • List Comprehension
  • Dictionary

Conditionals and Control Flow

  • If else
  • For Loop
  • Range
  • Break and continue


  • Lambda Expressions
  • Why Function
  • Writing simple functions
  • Advanced functions – map, reduce, filter and zip
  • Input, Output parameters

Errors, File handling

  • Syntax Errors
  • Exceptions
  • Date and Time
  • Input/Output
  • File Handling

Object-Oriented Programming

  • Introduction to Object Oriented Programming
  • Creating a Class
  • Generators & Iterators

Regular Expressions

  • Introduction to regular expressions
  • How to write a regular expression
  • Various operations on strings using re module
  • vs re.findall


  • Connection with database
  • Import data from CSV to database
  • Create, read, update, delete (CRUD)
  • Orderby
  • Groupby
  • Where
  • String operations
  • Joins

Data and Statistics

  • Elements, Variables, and Observations
  • Scales of Measurement
  • Categorical and Quantitative Data
  • Cross-Sectional and Time Series Data
  • Descriptive Statistics
  • Statistical Inference

Descriptive Statistics: Tabular and Graphical

  • Summarizing Categorical Data
  • Summarizing Quantitative Data
  • Crosstabulations and Scatter Diagrams

Descriptive Statistics: Numerical Measures

  • Measures of Location
  • Measures of Variability
  • Measures of Distribution Shape, Relative Location, and Detecting Outliers
  • Box Plot
  • Measures of Association Between Two Variables

Introduction to Probability

  • Experiments, Counting Rules, and Assigning Probabilities
  • Events and Their Probabilities
  • Complement of an Event
  • Addition Law
  • Independent Events
  • Multiplication Law
  • Baye’s theorem

Discrete Probability Distributions

  • Random Variables
  • Discrete Probability Distributions
  • Expected Value and Variance
  • Binomial Probability Distribution
  • Poisson Probability Distribution

Continuous Probability Distributions

  • Uniform Probability Distribution
  • Normal Curve
  • Standard Normal Probability Distribution
  • Computing Probabilities for Any Normal Probability Distribution

Sampling and Sampling Distributions

  • Simple random sample and its importance
  • Difference between descriptive and inferential statistics
  • Sampling distribution
  • Mean and standard deviation
  • Central Limit Theorem and its importance
  • Mean and standard deviation for the sampling distribution of the sample proportion
  • Sampling distributions of sample variances

Interval Estimation

  • Point estimate and confidence interval estimate
  • Construct and interpret confidence interval estimate
  • Form and interpret confidence interval estimate
  • Confidence Intervals for the Population Mean, μ
  • Confidence Intervals for the Population Proportion,  (large samples)

Confidence Interval

  • Point estimate and confidence interval estimate
  • Construct and interpret confidence interval estimate
  • Form and interpret confidence interval estimate
  • Confidence Intervals for the Population Mean, μ
  • Confidence Intervals for the Population Proportion,  (large samples)

Hypothesis Tests

  • Developing Null and Alternative Hypotheses
  • Type I and Type II Errors
  • Population Mean: Known
  • Population Mean: Unknown

Inference About Means and Proportions with Two Populations

  • Inferences About the Difference Between Two Population Means
  • Inferences About a Population Variance
  • Inferences About Two Population Variances

Simple Linear Regression

  • Simple Linear Regression Model
  • Regression Model and Regression Equation
  • Estimated Regression Equation
  • Least Squares Method
  • Coefficient of Determination
  • Correlation Coefficient
  • Model Assumptions
  • Testing for Significance
  • Using the Estimated Regression Equation for Estimation and Prediction
  • Residual Analysis: Validating Model Assumptions
  • Residual Analysis: Outliers and Influential Observations

Multiple Regression

  • Multiple Regression Model
  • Least Squares Method
  • Multiple Coefficient of Determination
  • Model Assumptions
  • Testing for Significance
  • Categorical Independent Variables
  • Residual Analysis

Model building in Regression

  • Regression model-building methodology
  • Dummy variables for categorical variables with more than two categories
  • Dummy variables usage in experimental design models
  • Lagged values of the dependent variable is regressors
  • Specification bias and multicollinearity
  • Heteroscedasticity and autocorrelation

Nonparametric Methods

  • Sign Test
  • Wilcoxon Signed-Rank Test
  • Mann-Whitney-Wilcoxon Test
  • Kruskal-Wallis Test
  • Rank Correlation

Tests of Goodness of Fit and Independence

  • Goodness of Fit Test: A Multinomial Population
  • Test of Independence


  • An Introduction to Analysis of Variance
  • Analysis of Variance:  Testing for the Equality of k  Population Means
  • Multiple Comparison Procedures
  • An Introduction to Experimental Design
  • Completely Randomized Designs
  • Randomized Block Design

Database Basics: Concepts and need of a database

  • What is a database?
  • What is SQL?
  • Database Learn Data Modeling
  • What is Normalization? 1NF, 2NF, 3NF & BCNF

SQL Fundamental: Selecting and Filtering Data

  • MySQL Installation
  • MySQL Create Database & MySQL Data Types
  • MySQL SELECT Statement
  • MySQL WHERE Clause with- AND, OR, IN, NOT IN

SQL Fundamental: Updating Data

  • MySQL query INSERT INTO Table
  • Sorting in MySQL ORDER BY, DESC and ASC

SQL: Data Aggregation and Functions

  • MySQL GROUP BY and HAVING Clause 
  • MySQL Wildcards : Like, NOT Like, Escape, ( % ), ( _ )
  • MYSQL Regular Expressions (REGEXP)
  • MySQL Functions
  • MySQL Aggregate Functions: SUM, AVG, MAX, MIN COUNT, DISTINCT

SQL: Complex Query Building

  • MySQL SubQuery
  • MySQL UNION – Complete

SQL: Query Optimization

  • Views in MySQL: Create, Join & Drop
  • MySQL INDEXES – Create, Drop & Add Index


Introduction to Data Science

  • Briefing about analytics domain
  • Business solving day to day problems using data
  • Technology platforms

Introduction to R programming

  • The basics of coding on R studio platform
  • R nuts and bolts
  • Basics of R programming
  • Installing predefined packages
  • Inputs and R objects (vector, matrix, dataframes and factors)
  • R datatypes
  • Using dplyr package
  • Text manipulations using Stringr
  • Reading data (csv file) in R

Data manipulations and looping in R

  • Data manipulations Subsetting dataset
  • Date and time in R
  • Loops: while & for
  • Conditionals: if-else
  • Functions: defining functions, anonymous functions
  • Apply family of functions

Exploratory analysis in R

  • Descriptive Statistical analysis
  • Sampling in R
  • Merging data
  • Reshaping data
  • Central tendencies
  • Measurements of Dispersion
  • Test of Normality
  • Null value treatment
  • Outlier treatment
  • Correlation analysis


RStudio Visualizations
  • Categorical data: Barplot,Pie chart
  • Numeric: boxplot
  • Histogram
  • Scatter plot
  • Line chart
  • Libraries like ggplot2, Rcolorbrewer
Interactive dashboard
  • Shiny for interactive graphical dashboards

Inferential Analysis in R

Parametric Statistical tests
  • Basics theory of inferential statistics
  • Hypothesis tests using Z test
  • T-statistics test
  • Two sampled z test and T test
  • Post-hoc test
Non-Parametric Statistical Test
  • Wilcoxen test
  • Mann-whitney U test
  • K.S. test
  • Runn Test
  • Chi-square test

Data Loading and file formats

  • Loading JSON files
  • XML and HTML web scraping
  • Interacting with HTML and web APIs
  • Interacting with Databases
  • Text mining/text analytics in R

Machine Learning

  • What is Machine Learning
  • Machine Learning Real World Example

Supervised learning techniques

Linear regression
  • Linear regression assumptions checks
  • Building Linear Regression model
  • Case Study- Linear Regression
Logistic Regression
  • Understanding Logistic Regression
  • Classification model building using logistic model
  • Confusion matrix
Random Forest

Decision Tree

Support Vector Machines

Naïve Bayes

Unsupervised learning techniques

K-means Clustering

Hierarchical Clustering

Time series analysis

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

100+ 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.

Basics of Python Programming

Milestone Project 1:

The first milestone project of this course is a very interesting analysis of the 2017 BWF World Championships – Women’s singles. The BWF World Championships is a badminton tournament sanctioned by Badminton World Federation (BWF). The programmer, having built the foundational skills in programming, will write a Python program that reads the statistics of the matches and compiles the scorecard in a structured format.

Milestone Project 2:

In this project, the learner will develop a Search Engine. This will enable to apply the fundamentals of parsing text data, building an index and enabling search.
There are in all 3 steps:
a) Data preprocessing
b) Building an inverted index
c) Finding the right index corresponding to a query

Data Cleaning and Analysis using Pandas

In multiple assignments, the learner will work with a very interesting dataset from Cricket – Indian Premier League. There are several questions based on the dataset that required to apply the techniques learned for:

  • Cleaning data
  • Using data frames for different tasks
  • Doing analysis
  • Using different techniques for working with data

Another set of assignments will make the learner work with the stocks data:

  • Do aggregations using pandas functions
  • Apply datetime funcionality
  • Apply different math functions
Data Visualization

Data Visualisation

The assignments enables a learner to work on trading data and looking at what the data tells using several techniques from matplotlib

  • Line Graphs to show stock price
  • Comparison of prices
  • Looking at high and low prices
  • Pie charts to understand mean prices
Exploratory Data Analytics Mini-project

In machine learning, you clean up the data and turn raw data into features from which you can derive the pattern. There are methods available to extract features that will be covered in upcoming sessions but it’s very important to build the intuition. The process of data cleaning and visualization helps with that. In this assignment, we will try to manually identify the important features in the given dataset.

Broad guidelines for the project:

  • View the data
  • Find the columns that are useful
  • Delete the columns that are not needed
  • Clean columns values and convert the column to numeric
  • Identify the columns containing useful information, they would be the features.
  • Visualize the important features
Machine Learning

The set of multiple assignments cover a wide range of algorithms with focus on many aspects of Machine Learning

  • Identify features
  • Check performance based on features
  • Build Labels
  • Split training and testing data
  • Create different type of Clusters and compare results
  • Supevised & un-supervised Learning algorithms
  • Regression & Classification
  • Natural Language Processing
  • Text Mining
  • Text processing and tokenization
  • WordCloud
Download curriculum (.pdf)
Get compelte details of this course

3 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.


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 hrs per week



13 Weeks



5 hrs per week

Upcoming batches starting on

Sep 14th, 2019 (Saturday)

10 AM -1:00 PM (IST)

Oct 20th, 2019 (Sunday)

10 AM -1:00 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.

Platform: Kaggle

Kaggle is an online community of data scientists and machine learners, owned by Google. Learners will be introduced and mentored to use the platform for practice and competitions.

Language: R

R is a language and environment for statistical computing and graphics. Learners will have the opportunity to build skills for using R for data science.

Tool: RStudio

RStudio provides open source and enterprise-ready professional software for the R environment. Learners will be using this editor for Data Science using R assignments.

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.

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.


Job Application

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


Interview Readiness

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

2 Certifications

Data Science with Python 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


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


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

Anshul Singh


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

Lipi Sahu


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 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.


Discuss with a Career Advisor

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