Post Graduate Program In Data Science

Data Science certification course is an extremely popular, in-demand profession which requires a professional to possess sound knowledge of analysing data in all dimensions and uncover the unseen truth coupled with logic and domain knowledge to impact the topline (increase business) and bottom-line (increase revenue). Also, Google Trends shows an upward trajectory with an exponential increase in the volume of searches like never seen before. This is proof enough to back the statements made by Harvard Business Review and the business research giants, that Business Analytics will be the most sought-after profession the world has ever witnessed.

Data Science / Analytics is creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing historical data in order to make informed decisions and improve their businesses.

Data Science has gained a cult status
amongst working professionals and grad students alike

"Steinbeis University, Berlin Accreditation, It is continuing faithfulness in the founding and development of this institution and outstanding competence, and excellence in deliveries".

Steinbeis University Berlin works as a knowledge and technology transfer partner to companies in trade and industry. Its core services encompass: research and development, consulting, and – as a basis for all of this – education. Founded in 1998, the Steinbeis University Berlin (German abbreviation: SHB) offers executive degrees and employee training and development programs matched to the needs

of ‘knowledge and technology transfer’ – that is ideally suited to the requirements of modern, knowledgebased society.
Steinbeis University aims to increase competitiveness among the students and the companies beyond the pure knowledge transfer and application.

Steinbeis accreditation recognizes ExcelR’s excellence in areas such as curriculum, faculty qualifications, support services, institutional effectiveness, planning and learning resources and student learning outcomes.

ExcelR’s Post Graduation Program in Data Science

ExcelR’s Data Science Post Graduate curriculum is meticulously designed and delivered matching the industry needs and considered to be the best in the industry.

All our trainers have extensive experience as Data Scientists in leading Multinational companies and have passion for teaching and considered to be the best in the industry. At ExceIR, we hand pick the trainers subsequent to a thorough evaluation of knowledge, presentation skills, experience and passion for training. No wonder that our trainers are the best in the industry. Participants can be rest assured about the real-life practical exposure along-side with the theory.


Graduates from any stream with good logical, mathematical and analogical skills. Working Professional from any domain, who has good logical, mathematical and analytical skills

The course is for professionals working on Business intelligence, Data Warehousing and reporting tools to improve their knowledge in data science. Engineering freshers with the qualification they are able to work as data analyst / Business Analyst: “entry-level” position in the data science field


Introduction to core python Programming

  • Overview of Python-Starting with Python
  • Why Python for data science?
  • Anaconda vs. python
  • Introduction to installation of Python and Packages
  • Introduction to Python Editors & IDE's(Jupyter,/Ipython)
  • Understand Jupyter notebook & Customize Settings
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Debugging & Code profiling
  • Built-in Functions (Text, numeric, date, utility functions)
  • User defined functions – Lambda functions

Data science Project Lifecycle

  • Introduction to Types of analytics, project life cycle

Github and Kaggle

  • Intro to Github and Kaggle and accounts creation

Introduction to R Programming

  • Overview of R - Starting with R
  • Installation R and Rstudio
  • Data Types & Data structures
  • Data Importing and Exporting

Basic Statistics

  • Data Types, Measure Of central tendency, Sampling Funnel
  • Python DS libraries Pandas, Numpy, Scikit, matplotlib)
  • Measures of Dispersion, Expected ValueR coding
  • Random Variable, Probability, Probability Distribution (Normal and Logistic)R coding; Graphical Techniques (Bar, Boxplot and histogram etc)
  • R coding
  • Skewness & Kurtosis, Sampling Variation

Interferential Statistics

  • CLT, Confidence interval
  • R coding
  • Introduction to concept with examples( 2 proportion test, 2 t sample t test)
  • Python DS coding concepts and challenges
  • Anova and Chisquare case studies
  • Python DS coding challenges

Logistic Regression

  • Principles of Logistic regression
  • Python DS coding challenges
  • Multiple Logistic Regression, ROC curve, Gain chart, Chisquare theory hands on Python DS coding challenges

Data Mining - Unsupervised

  • Clustering – Hierarchical
  • Python DS coding challenges
  • Clustering – Kmeans
  • Python DS coding challenges
  • Unsupervised - Network Analytics(update the code in better way)
  • Association Rules
  • Recommender System;


  • Introduction to Timeseries, Level, Trend and Seasonality, strategy (Python DS coding challenges)
  • Scatter plot, Lag plot, ACF, Principles of Visualization, Naïve forecasts (Introduction to R shiny (deployment)
  • Forecast in Error and it metrics, Model Based Approaches (Introduction to Python flask (deployment)
  • Model Based approach cont, AR Model for errors
  • Data driven approaches, MA and exp Smoothing

Survival Analysis

  • Concept with a business case

Machine Learning additional topics

  • Train, Test & Validation Distribution
  • ML Strategy, Computation Graph
  • Evaluation Metric, Human Level Performance (Python DS coding concepts and challenges)


  • Calculus (Python DS coding concepts and challenges)
  • Linear Algebra; Probability

Intro to Neural Network & Deep Learning

  • Introduction- Deep Learning Importance [Strength & Limitation] and SP | MLP

Feed Forward & Backward Propagation

  • Neural Network Overview
  • Neural Network Representation and Activation Function
  • Loss Function
  • Importance of Non-linear Activation Function and Gradient Descent for Neural Network

Parameter & Hyperparameter

  • Train, Test & Validation Set
  • Vanishing & Exploding Gradient
  • Dropout and Regularization


  • Bias Correction
  • RMS Prop
  • Adam
  • Ada
  • AdaBoost
  • Learning Rate
  • Tuning
  • Softmax

Python Environment for Deep learning

  • NLTK
  • Spacy & Gensim
  • OpenCV
  • Tensorflow and Keras

Data Processing - Text Processing

  • Representation
  • Data Cleaning
  • Data Preprocessing and Similarity

Data Processing - Image Processing

  • Image & Image Transformation and Filters
  • Noise Removal
  • Correlation & Convolution
  • Edge Detection
  • Non Maximum Suppression & Hysterisis Fourier Domain
  • Video Processing

Stored Procedures and Functions

  • Feature Extraction
  • Image Feature
  • Descriptors

Object Detection

  • Detection & Classification


  • Computer Vision
  • Why Convolution
  • Convolution
  • Padding
  • Pooling

CNN - Deep Convolution Model

  • Case Studies
  • Classic Networks;
  • Inception
  • Open Source Implementation
  • Transfer Learning

CNN - Detection Algorithm

  • Object Localization
  • Landmark Detection
  • Object Detection
  • Bounding Box Prediction
  • Yolo

CNN - Face Recognition

  • What is Face Recognition
  • One Shot Learning
  • Siamese Network
  • Triplet Loss
  • Face Verification
  • Neural Style Transfer
  • Deep Conv Net Learning

Sequence Models

  • Why Sequence Model
  • RNN Model
  • Back propagation through time
  • Different Type of RNNs
  • GRU,LSTM, Bi-directional LSTM
  • Deep RNN
  • Word Embedding
  • Debiasing
  • Negative Sampling
  • Elmo & Bert
  • Beam Search
  • Attention Model

Generative and Reinforcement Learning

  • Autoencoders & Decoders
  • Adversial Network
  • Active Learning
  • Q Learning
  • Exploration & Exploitation


  • Concepts ; Assignments, Project

DS recap and Coding

Duration 330 Hours


Examination details

1 Pattern 100 Multiple Choice Questions
2 Topics Data Science 100%
3 Time in minutes 150
4 Mode Online - Computer Based(Web proctoring)
5 Pass Percentage 60%
6 Number of attempts allowed 2
7 When will be the examination Every second Sunday of a month
8 Criteria 80% attendance
9 Mock tests 2

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