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Course Introduction

This course spans 4 months + 1 month Specialisation

Data science became a career buzzword for IT professionals when a Harvard Business Review termed it as "The Sexiest Job of the 21st Century". According to the U.S. Bureau of Labour Statistics, growth for data science jobs skills will grow about 30% year on year through 2026. At this point in time in 2021, the average salary of a Data Scientist / Analyst is to the tune of US$ 120K annually.

Why Data Science is a sought after skill for employers: Companies across industries are rushing towards automated decision making for all of their departments and that is possible through the application of various Data Science, Analytics, Machine Learning and Deep Learning skills. Every functional field in any company are seeking out opportunities to keep making their systems smarter and decisions smarter - be it automated voice assistants, smart cars, smart factories, smart marketing and advertising, smart sales predictions, smart investment decisioning and many more.

Skills Required for Data Scientists

If it is your goal is to become a Data Scientist, you have to first understand what it takes to become one, the skills and competencies that you should learn. Data Science is an amazingly interesting field, full of interesting concepts and power to create magic from Data.

Comprehensive knowledge on Deep Learning, ML-Ops and AI/ML Product Development are critical knowledge areas for any Data Scientist/Data Engineer/Machine-Learning Professional. This course places a lot of focus into these areas so that there is no learning gap when you start on a Data Science/Machine Learning role.

The curriculum prepares you to be a leader in this field through mastery of core data science concepts like Statistical Analysis of Data, Exploratory Data Analysis Techniques using Python, powerful Visualizations, Machine Learning, Deep Learning and Model Deployment in Production. By diving deep on key topics as above in a fully practical way, you'll be prepared to succeed within today's organizations. You'll also work with real data sets from top companies as you build a work portfolio that showcase your skills. Learn the systems and techniques that help organizations overcome data overload and make smart decisions.

Curriculum Approach

Curriculum for this Applied Data Science program has been modelled around the life-cycle of Data Science Projects. There is special emphasis on selection of Languages, Algorithms, Libraries, Tools, Projects and Assignments that enable our learners to achieve that. There is special emphasis on Deep Learning and applications of Deep Learning for solving complex problems, ML-Ops so that learners have a hands-on exposure on what happens in real-life situations in terms of going live with your models and maintaining them in production. We also spend a week on industry trends and ML/AI product development practices and mechanics. These topics and exposures will enable our learners with the ability to take up a wide-variety of challenges and job roles.

Course Features

Exploratory and Colloborative Programming

Business Domain Understanding

Quizzes, Assignment and Capstone


Career Oriented Course

Pre-requisites and Students Backgrounds

Renowned Faculty

Program Structure

Program Duration
Post Graduate Program on Applied Data Science with Deep Learning and Specialisation (TEKS-RISE) 4 Months
Specialisation Options (R Language, Tableau) 1 Month
Mode Days/Timings
Live Online, Instructor Led Weekend Batches / Morning 10:00 am - 1 pm (Saturday/Sunday)


Data Acquisition Techniques

Exploratory Data Analysis using Pandas, Numpy libraries

Visualizations using Matplotlib, Seaborn libraries

Modelling and Predictive Techniques - Supervised Learning, Unsupervised Learning, Prediction Problems, Classification Problems, Clustering using Scikit Learn, StatsModel libraries

Applied Part - Portfolio Projects

Deep Learning with CNN, RNN, LSTM, Attention models using Tensorflow and Keras

ML-Ops on GCP platform

Data Science Leadership

Python will be used as the primary programming language throughout the course. Teksands will organise pre-course Python sessions for those with little or no exposure to Python.

Libraries Covered

Real Life Projects
Real Life Projects
Real Life Projects
Real Life Projects
Real Life Projects
Real Life Projects
Real Life Projects
Real Life Projects
Real Life Projects
Week 1: Introduction to Data Science
6 Topics
Lesson Content

Introduction to Applied Data Science

Domains, Business and Data

Use Cases and Problem Statement understanding

Data Science Project Life Cycle

Exploratory and Colloborative Programming


Week 1-2: Collecting and Processing Data
7 Topics
Lesson Content

Advanced Data Structures and Manipulation using Python

RDBMS and writing SQL

Working with Files

Processing Big Data

Experiment Design and Analysis

Data Analysis using Excel


Week 2-3: Data Science Concepts
7 Topics
Lesson Content

Supervised and Unsupervised Learning

Predictive Modelling / Machine Learning

Types of Algorithms

Scenario-Algorithm Selection

Model Training Selection

Cost Functions


Week 4: Statistical Techniques
5 Topics
Lesson Content

Descriptive Statistics

Inferential Statistics

Probability Theory

Tests of Significance


Week 4-5: Exploratory Data Analysis
8 Topics
Lesson Content

Statistical Techniques

Linear Algebra

Visual Exploration

Data Mining

Feature Engineering

Machine Learning Pipelines

Causal Inference


EDA Project Assignment

Week 5-6: Predictive Modelling / Machine Learning 1 - Linear Regression
9 Topics
Lesson Content

Solving Prediction Problems through Linear Regression

Data Exploration

Pre-processing and Feature Engineering

Creating an LR model

Optimizing Models through RFE and VIF methods

Testing LR Assignments

LR Project Assignment


Week 6-7: Predictive Modelling / Machine Learning 2 - Logistic Regression
8 Topics
Lesson Content

Estimating Probabilities

Logistic Regression Cost Functions

Softmax Regression

Performance Matrix

ROC Curve and AUC

Optimizing Logistic Regression


Logistic Regression Project Assignment

Week 7-8: Predictive Modelling / Machine Learning 4 - Decision Tree and Random Forest
8 Topics
Lesson Content

Introduction to Decision Trees

Gini Index and Entropy

Measuring Performance

Introduction to Random Forest

Random Forest Process Steps

Model Performance and Tuning


Random Forest Project Assignment

Week 8: Predictive Modelling / Machine Learning 5 - Dimensionality Reduction
9 Topics
Lesson Content

Introduction to PCA(Predictive Component Analysis)

PCA Process Steps

Data Standardization

Finding Covariance Matrix of our Dataset

Eigen Vectors and Eigen Values

Recast Data using new PCAs

Expained Variance Ration and Screen Plot


PCA Project Assignment

Week 9: Predictive Modelling / Machine Learning 6 - Advanced Classifier: SVM
6 Topics
Lesson Content

Introduction to Support Vector Machine

Linear SVM Classifications

Non-Linear SVM Classification

Polynomial Kernel Trick


SVM Project Assignment

Week 9-10: Predictive Modelling / Machine Learning 7 - Clustering using K-Means
10 Topics
Lesson Content

Unsupervised Learning

Introduction to PCA

PCA Process Steps

Centroid Optimizing / Convergence

Other Considerations

Optimizing Number of Clusters


K-Means Project Assignment

Week 11: Neural Networks and Deep Learning
8 Topics
Lesson Content

Neural Nets

Perceptron and MLP (Multi Layered Perceptron)

Introduction to TensorFlow

Introduction to Keras

Classification MLPs

Regression MLP


Deep Neural Network Project Assignment

Week 12: CNN and Image Processing
8 Topics
Lesson Content

CNN Architectures

Training a CNN

Image Pre-processing Techniques

Transfer Learning with CNN

TensorFlow Implementation of CNN

Object Detection


Object Detection Project Assignment

Week 13: RNN and LSTM
7 Topics
Lesson Content

RNN Architectures/p>

Training an RNN

Unstable Gradients Challenge

LSTM Architecture

Training and Predicting a Time Series


LSTM Project Assignment

Week 14: Natural Language Processing with RNN and Attention Models
7 Topics
Lesson Content

Generating Text using a RNN

Bidirectional RNN

Attention Mechanism

Encoder-Decoder Models

Natural Machine Translation using an Encoder-Decoder Model


NLP Project Assignment

Week 15: ML-Ops using Google Cloud Platform
7 Topics
Lesson Content

Understanding ML Lifecycle

Creating Docker Containers for ML Deployments

Manage Kubernetes Deployments

Setting up AI Pipelines

Training, Tuning and Serving a Model

Kubeflow Pipelines

CI/CD on KubeFlow Pipelines

Week 14-17: Capstone Project
3 Topics
Lesson Content

Choice and Briefing of Capstone Projects

Research, Analysis and Methodology

Presentation Techniques - Paper and Visual

Week 16: Deep Learning Product Development
5 Topics
Lesson Content

Current AI Trends

Start-ups and Products in Data Science/AI/ML

Upcoming Genre of Products

Understanding AI Products Development Lifecycle

Deep Learning Market

Option 1: Tableau
11 Topics
Lesson Content

Create various Charts and Graphs

Create Dashboards

Create Data Hierarchies


Working with Parameters

Creating Data Extracts

Applying Filters

Calculated Fields

Mapping in Tableau

Advanced Charts

Publish to Tableau Online

Option 2: R Language
9 Topics
Lesson Content

Fundamentals of R






Visualisations using GGPlot2

Creating a Linear Regression Model using R

Creating a Logistic Regression Model using R

Real Life Projects

Real Life Projects
Price Prediction Problems
Real Life Projects
Customer Churn Analysis
Real Life Projects
Credit Card Fraud Detection
Real Life Projects
Email Spam Filtering
Real Life Projects
Sentiment Analysis using NLP Techniques
Real Life Projects
Product Recommender Engine
Real Life Projects
Social Media Analysis
Real Life Projects
Image Recognition
Real Life Projects
Driver Drowsiness Detection
Real Life Projects
Price Prediction System
Real Life Projects
Text Similarity Detection
Real Life Projects
Behavioural Segmentation
Real Life Projects
Fake News Identification
Real Life Projects
Image Captioning


Data Science and Predictive Analytics has served a multitude of functions and job needs and a lot of Job Roles are created in organizations in the last few years. Some of the prominent Job Roles in this space are listed below:

Data Scientist: Data Scientists would have the responsibility of understanding and analysing all the data the organisation has and create Data Driven products and solutions to create businesses processes more efficient, drive automation, create decision systems, future prediction systems, etc.

Data Architect: Data Architects would typically analyse the organisational Data Schemas, design new schemas for newer data driven systems , tune existing data schemas, optimise organisational Mete Data and all data repositories including ETL Systems.

Data and Analytics Manager: Responsible for managing and leading Data initiatives in the organisation, including leadership in ETL programs, Decision Systems programs, leading analytics teams, etc.

Data Analyst:Data Analysts typically gather and analyse data within divisions and organisation for the purpose of building Insights and Analytics solutions and systems using a range of tools, techniques including statistics. This role is highly important for the leadership of any organisation to develop understanding of business trends.

Machine Learning Engineer: Responsible for developing sophisticated Machine Learning Models that are to create various Decision, Prediction, Classification, Clustering systems on Business Data. All the roles above and the plethora of roles this space is offering are growing rapidly in demand and skills shortfall is even expanding leading to high salaries for every skilled personnel in these fields.

Given "Data is the new Fuel", demand for professionals in these fields in the many years to come will continue to expand unabated creating massive opportunities for data professionals.

With Data Science applications booming through businesses leading to saving costs, better profitability and driving newer business models and products, the demand for these skills have skyrocketed. Literally, every business today is after quality skilled professionals in Data Science and Analytics. Not only they are looking for Data Science and Predictive Analytics skills to create new solutions, but preferring these as must-have skills in all other fields to drive continuous automation and efficiency. Even Business and Operations personnel are today are equipping themselves with foundational knowledge in these areas to save costs through automation.

Some statistics:

  • 70-80% Year on Year New Job Numbers Growth in Data Science and related skills
  • 15-20% Year on Year Average Salary Growth in these fields
  • 85% of the Companies are Investing and expanding their Data Science Teams rapidly
  • In 2020-21, there is a net shortage of 250,000+ skilled resources in these fields
  • 2 Years is approximate Data Science Staff Tenure in companies

The course is completely based on practical approaches of teaching. Learners will have intense exposure to real code and data while learning the concepts on the go. We will also provide you all the codes used in training and also additional problems for you to work on and practice.

The Delivery method is Online, Live Classes led by Professional, Industry Experienced Instructors.

4 Months + 1 months Specialisation

Weekend Courses: Weekend Batches / Morning 10:00 am - 1 pm (Saturday/Sunday).

(Please check your specific course schedule)

  1. Laptop with Windows 7, 8, 10 / MacOS / Linux
  2. Internet Connectivity
  3. Latest Chrome / Firefox Browser
  4. Microsoft Excel
  5. Python Version 3 or above (https://www.python.org/downloads/)
  6. Anaconda Platform (https://www.anaconda.com/distribution/)

All courses on Teksands are taught by Industry Professionals, highly qualified and focused Research Scholars from Reputed University

Yes, Teksands will help you in securing a placement after successful completion of the course.

Teksands Post Grad Program on Data Science focusses on students building a Portfolio of Industry Projects across all learning areas. This portfolio helps you demonstrate your ability to prospective employers and make a difference. Additionally, Teksands course will have important industry-related modules such as ML-Ops and AI Product Development which gives you critical edge in launching a successful career journey.

Yes, please speak to your career counsellor to get to know more on instalment schemes.

This is a career focussed course and to be taken very seriously by the students. We recommend a minimum of 10 hours study per week beyond the online classrooms to as much as possible. Your commitment will help you succeed.


Upon successful completion of the programme, participants will be awarded a verified digital certificate by Teksands.

Get Certified

Teksands.ai is a fast growing start-up in the EdTech Industry. We provide Online Courses on Deep Tech including Data Science, Machine Learning, Artificial Intelligence, Python, Deep Learning, Neural Network, to name a few. Our courses are intended to primarily help working professionals achieve career augmentation or career switch in Deep Tech areas by delivering very high quality, application driven training suited to the needs of our learners needs and goals.