Program Highlights

  • Essential Stats, Math by highly qualified and experienced faculty
  • Conceptual clarity of Machine Learning and related techniques
  • Get Practical knowledge of Machine Learning
  • Professionally designed for both fresher’s and working professionals
  • Gain Expertise in Data Visualization and Data Analytics widely used in various Business Intelligence Processes.
  • Upgrade your skillset with highly secure, cost effective and portable Database Management System with seamless connectivity.

Why Data Science ?

Data science has emerged as an attractive career option for freshers as well as experienced professionals. The demand for data engineers is very high in sectors like information technology, telecom, manufacturing, finance and insurance, retail and many more.

It is most demanded job in 2021. Big Data Engineers, Machine Learning Engineers, and Data Scientists are the top three emerging jobs on LinkedIn.

As predicted, by 2026 data science and analytics would be having more than 11 million jobs.

India is the second prominent hub of jobs for data scientists.

Ever since 2012, the job positions for Data Scientists have increased by over 650%

Who Can Join?

  • IT Professionals
  • Analytics Managers
  • Business Analysts
  • Banking and Finance Professionals
  • Beginners or Recent Graduates in Bachelors or Master’s Degree
  • IT Fresher’s


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Python Programmingsec7accordion icon40 Hours
  • Download and Install Anaconda Navigator, Different IDE's of Python, Introduction to Jupyter Notebook
  • Basic Python Programming (Syntax, Comments, Variables, Data types, Operations)
  • Data Structures (List, Dictionary, Set, Tuple and operations)
  • Loos and statements (if else, for, while, continue, break)
  • In built functions, User defined functions, Lambda function
  • Handling date time data
  • Exceptions handling
  • Introduction to Advanced Python Programming
  • Some useful libraries/packages: Numpy, Pandas
Essential Mathsec7accordion icon16 Hours
  • Matrix Algebra
  • Types of matrices
  • Algebraic Operations on matrices
  • Transpose, Determinant, Inverse of matrix
  • Solving System of Linear Equations
  • Eigen values Eigen vectors
  • G inverse
  • Singular Value Decomposition
  • Applications of Matrix algebra in Business cases
  • Calculus
  • Mathematical Functions
  • Derivatives
  • Integration
  • Gradient Descent Algorithm
  • Applications of Calculus algebra in Business cases
Essential Statisticssec7accordion icon30 Hours
  • Introduction, Types of data, Visualizing different types of data, Summarise different types of data
  • Random variable, Probability and Probability distributions
  • Bernoulli, Binomial, Poisson distribution
  • Normal distribution
  • Population and Sample
  • Sampling Distributions
  • Tests of hypothesis (t test, Chi square test, F test for mean, variance, proportion, attribute dependency)
  • Correlation and Simple Linear Regression
Data Visualizationsec7accordion icon10 hrs
  • Data Visualization Libraries in Python
  • Pandas, Matplotlib, Seanborn
  • Data Visualization for different types of data
  • Bar chart, Pie chart, Histogram, Density plot, scatter plot, scatter matrix
R Programmingsec7accordion icon30 hrs
  • Download and Install R, R studio
  • Basic R Programming (Syntax, Comments, Variables, Data types, Operations)
  • Data Structures in R
  • Loos and statements (if else, for, while, continue, break)
  • In built functions, User defined functions
  • Handling date time data
  • Handling text data
  • Statistics and Data Visualization in R
Feature Engineering, Dimension Reductionsec7accordion icon12 hrs
  • Transformations on data depending on it’s type
  • Min max, Binary, Discretise, Standardize, Log and Power transformations, One hot encoding, Zero One.
  • Principle Component Analysis
  • Singular Value Decomposition
  • Different ways to identify important columns and removing irrelevant columns
Machine Learningsec7accordion icon44 hrs
  • Basics of Machine Learning, Types of Machine learning, Regression, Classification, Model building techniques, Model evaluation techniques
  • Supervised Learning: Multiple Linear Regression, Logistic Regression, Naïve Bayes Classifier, K Nearest Neighbour, Decision Tree, Bagging, Random Forest, Boosting, Support Vector Machine, Artificial Neural Network, Deep Learning
  • Unsupervised Learning: principle Component Analysis, K means Clustering, Hierarchical Clustering
  • Transfer Learning, Model Deployment Basics
  • Real Life Applications
Artificial Intelligence & Computer Visionsec7accordion icon30 hrs
  • Recap of ML, Supervised and Unsupervised learning
  • Reinforcement Learning: Basics of RL, Markov Decision Process, Q learning
  • Introduction to CV
  • Working with images
  • In built models to work with images (Object detection, recognition)
  • Building models for image classification
Tableausec7accordion icon20 hrs
  • Data Visualization
  • Working with data
  • Working With Filters
  • Parameters
  • Graphs And Charts
  • Stories and dashboards
  • Integration with R
My SQLsec7accordion icon30 hrs
  • Introduction
  • Features and Benefits of MySQL
  • Install and Start MySQL
  • Database Basics
  • SQL Language and MySQL
  • Database Design
  • Database Modeling
  • Keys
  • Normalizatio
  • Data Types
  • What is NULL?
  • Viewing Database Structure
  • Database Creation
  • Create a New Database Structure
  • Creating a Database
  • Creating a Table
  • Basic Queries
  • The SELECT Command
  • MySQL Query Browser
  • Troubleshooting
  • Database Maintenance
  • Delete an Entire Database
  • Maintaining Tables
  • Maintaining Columns
  • Indexes and Constraints
  • Data Manipulation
  • Delete/Modify Table row Data
  • The INSERT Command
  • The REPLACE Command
  • The UPDATE Statement
  • The DELETE Command
  • Functions
  • Simple Functions
  • Grouping With Functions
  • Joining Tables
  • Combining Multiple Tables
  • Inner Joins
  • Outer Joins
  • Exporting/Importing Data
  • Exporting Data
  • Importing Data
  • Sub queries
  • What is a Sub query?
  • Categories of Sub queries
  • Sub query Types
  • Placing Sub queries
  • Other Sub query Uses
  • Other Sub query Uses
  • Supplementary Information
  • Creating VIEWs
  • Transactions
  • Storage Engines
  • Retrieving Metadata

Program Deliverables

  • Programming
  • Statistical Data Analysis
  • Machine Learning
  • Artificial Intelligence
  • Tableau
  • MY SQL
  • Build a Predictive model using ML techniques for important business problems

Tools and Techniques, you will master

Salary Trends

Career Opportunities

(Industry Wise)

Faculty Profile

Qualified and experienced faculty in their respective areas.

Mr Sachin Adnaik

Senior Data Scientist

  • Ph.D. in Statistics
  • 10 years of training experience
  • 8 years of experience in Data Analytics
  • 8 years of experience in Machine Learning
  • Experienced in handling/working with image, genetics and text data

Mr. Atul Phad

Head of Technology,JARS Online Retail Solutions Pvt Ltd

  • BE (Computer) with 9 + years of work experience
  • Amazon Web Service Solution Architect
  • Micosoft Technology Associate-SQL
  • Microsoft Azure Certified
  • Expert in Tableau, Apache, Hadoop, ETL, Python, Flutter etc

Rohit Agrawal

Data Base Administrator

  • Oracle Certified Database Administrator
  • Expert in Python, Big-Data, MongoDB
  • Corporate Trainer

Batch Commencement- 2nd May 2021

Total Program Fee

Rs. 66000/- ( All Inclusive)

Faster payment with

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Batch Schedule

Saturday -2 pm - 6 pm
Sunday -2 pm - 6 pm

Frequently Asked Questions

What is PGD Data Science program intended to provide?
Data science is mainly about figuring out trends and patterns based on statistics and jumbled data. You will learn all about the data science, from Python to Machine Learning & more. Expand your career horizon by adopting data science practices in various organizations.
How is the program delivered?
The PGD Data Science is a practice based 7 Months long online integrated program.
When does the program begin?
PGD Data Science Program starts from 2nd May 2021
Is there a refund policy for the program?
There will be no refund
I have more queries. Whom can I connect with?
For more queries please write us at or call on +91 9130070132
What is the minimum educational qualification required for the program?
Any Graduate in any discipline with a minimum of 50% aggregate marks can apply for this program
What topics will I learn in the program?
You will learn Programming, Statistical Data Analysis, Machine Learning, Artificial Intelligence and Build a Predictive model using ML techniques for important business problems

About APG Learning

APG LEARNING– A Division of Sakal Media Group APG Learning is the Skilling and Education vertical of the AP Globale Group that has its presence across different businesses including Media, Advisory, and Community Development.

APG Learning began operations a few years ago with a mission to educate students and ensure sustainable socio-economic development. We run some of the most successful employability-related courses in the fields of Media & communication, Business, Entrepreneurship, Finance,Technology& Lifestyle.

Our programs are designed to build entrepreneurs and leaders, who will go on to have a substantial social impact on economic development. With a faculty drawn from the industry as well as academia, learning at APG Learning melds together real-time experiences with academic knowledge. Also, APG Learning has kept up with the pace of Teaching in the Digital Age with our segment of online training as the approach towards teaching and learning has drastically changed with the advent of technology.

Contact Info

For any queries regarding admissions for the PGD Data Science program, or to establish industry or academia relationships, or additional assistance

reach out at contact or +91 9130070132