Grade "A+" Accredited by NAAC with a CGPA of 3.46
Grade "A+" Accredited by NAAC with a CGPA of 3.46
Certificate Program

Data Analytics & Business Intelligence

About The Course

Data Analytics & Business Intelligence is the need of the hour. Today, huge amounts of data are being generated without knowing how to make beneficial use of it. In order to utilize this, machine learning and statistical techniques are being used to develop predictive models from existing data to forecast future outcomes.


Data analytics and business intelligence are of great importance in today’s world.

Data analysis is required to understand organizational problems and to explore data while business intelligence helps companies to make better decisions by showing current and historical data within their business context. To make the organization run smoother and more efficiently, analysts use BI to provide performance and competitor benchmarks.


The fundamental aim of leveraging Data Analysis and Business Intelligence skills is to help understand  trends and derive actionable insights from data, thus allowing to make data-driven, strategic and tactical business decisions.

Data Analytics & Business Intelligence

Course Coordinator

Dr. Rishi Rajan Sahay

Email :
Phone : 
Batch Size : 
35-45 students
Timing : 
To be updated
Venue : 
SSCBS Campus, Dr. K N Katju Marg, Sec 16, Rohini, Delhi
Eligibility Criteria : 
To be updated
Program Fees : 
Rs 40,200 per student
Program Duration : 
125 Hours

Admission Notice About The Course Application Form

Learning Outcomes

Students will be able to apply principles of statistics, python programming, machine learning, probability and decision making in the context of data analysis. Moreover, they should design tested and effective advanced analytics models for decision making and communicate effectively in a variety of modes and contexts.


Expecting to build a solid business analytics foundation, this course has been designed to impart knowledge of machine learning and statistical methods for data analysis. The course shall also provide sufficient knowledge of python programming language for machine learning algorithms and python/ R programming for statistical methods. A brief introduction to neural networks and deep learning will also be covered.

Target Audience

Those who are interested in developing a strong foundation in business analytics and have graduated or are pursuing graduation (studied mathematics till class 12th level).

Future Prospects

Upon completion of the course, the students will be able to enhance their skills in data analysis, python programming for machine learning and python/ R programming for statistical methods. They will also be able to find answers to the questions they don’t know the answers to. This course will help them to adapt themselves to the automated future of business intelligence.

Resource Persons

  1. Eminent resource persons from reputed institutes will be invited.
  2. The experts from various industries will also be invited for delivering lectures and hands-on

Mode of Fee Payment

By Cheque or Net Banking (Preferred)

Account Name: Principal SSCBS Students Society A/C

Bank: State Bank of India

Account Number: 35810781108

IFS Code: SBIN0011550

MICR: 110002303

Branch: (11550)- Pascon Building Garg Trade Centre, Sector-11, Rohini, New Delhi-110085

Brief Profile of Participating Institutions

Shaheed Sukhdev College of Business Studies (CBS) the first undergraduate management school under the aegis of the University of Delhi (DU) is one of the premier institutions imparting education in the fields of management and information technology with excellence and vision. Visit:

Course Syllabus

Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
Module 7
Module 8

Title: Foundation of Data Analytics & Python Programming

Duration (Hours): 20

Foundation of Data Analytics: – Introduction, Evolution, Concept and Scopes, Data, Big Data, Metrics and Data classification, Data Reliability & Validity, Problem Solving with Analytics, Different phases of Analytics in the business and Data science domain, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics , Different Applications of Analytics in Business, Text Analytics and Web Analytics, Skills for Business Analytics, Concepts of Data Science, Basic skills required for understanding Data Science.

Python Programming: – Introduction to Python Editors & IDE’s (Jupyter, Spyder, PyCharm, etc…), custom environment settings, basic data types -numeric, string, float, tuples, list, dictionary, sets and their operations, control flow (if-elif-else), loops (for, while), inbuilt functions for data conversion, writing user defined functions.

Concepts of packages/libraries – important packages like NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc., installing and loading packages, reading and writing data from/to different formats, simple plotting, functions, list comprehensions, database connectivity, Playing with Date Format.

Title: Probability & Statistics

Duration (Hours): 25

Descriptive Analytics: Describing and summarizing data sets, measures of central tendency, dispersion, skewness, kurtosis, Correlation.

Probability: Measures of probability, conditional probability, independent event, Bayes’ theorem, random variable, discrete (binomial, Poisson, geometric, hypergeometric, negative binomial) and continuous (uniform, exponential, normal, gamma). Expectation and variance, markov inequality, chebyshev’s inequality, central limit theorem.

Inferential Statistics: Sampling & Confidence Interval, Inference & Significance. Estimation and Hypothesis Testing, Goodness of fit, Test of Independence, Permutations and Randomization Test, t- test/z-test (one sample, independent, paired).

Title: Data Munging with Python

Duration (Hours): 15

Relevance in industry, Statistical learning vs machine learning, types and phases of analytics.

Data pre-processing and cleaning: data manipulation steps (sorting, filtering, duplicates, merging, appending, subsetting, derived variables, data type conversions, renaming, formatting, etc.), normalizing data, sampling, missing value treatment, outliers.

Exploratory data analysis: Data visualization using matplotlib, seaborn libraries, creating graphs (bar/line/pie/boxplot/histogram, etc.), summarizing data, descriptive statistics, univariate analysis (distribution of data), bivariate analysis (cross tabs, distributions and relationships, graphical analysis).

Title: Machine learning – Part 1

Duration (Hours): 17

Introduction, Applications of Machine Learning, Key elements of Machine Learning, Supervised vs. Unsupervised Learning.

Supervised Machine Learning: Linear Regression, Multiple Linear Regression Polynomial Regression.

Classification: Using Logistic Regression, Logistic Regression vs. Linear Regression, Logistic Regression with one variable and with multiple variables, Application to multi-class classification. The problem of Overfitting, Application of Regularization in Linear and Logistic Regression. Regularization and Bias/Variance. Classification using K-NN, Naive Bayes classifier, Decision Trees (CHAID Analytics), Random Forest, Support Vector Machines.

Natural Language Processing (NLP): Definition and scope of NLP, Applications of NLP in data analytics, Text classification, sentiment analysis

Model Evaluation: Cross validation types (train & test, bootstrapping, k-fold validation), parameter tuning, confusion matrices, basic evaluation metrics, precision-recall, ROC curves.

Case study

Title: Machine learning – Part 2

Duration (Hours): 18

Neural Networks: Introduction, Model Representation, Gradient Descent vs. Perceptron Training, Stochastic Gradient Descent, Multiclass Representation, Multilayer Perceptrons, Backpropagation Algorithm for Learning, Introduction to Deep Learning.

Association Rule Mining: Mining frequent itemsets, Apriori algorithm, market basket analysis.

Case study

Unsupervised Machine Learning: Introduction, Clustering, K-Means algorithm, Affinity Propagation, Agglomerative Hierarchical, DBSCAN, Dimensionality Reduction using Principal Component Analysis.

Case study: Application of PCA

Time Series Forecasting: Trends and seasonality in time series data, identifying trends, seasonal patterns, first order differencing, periodicity and autocorrelation, rolling window estimations, stationarity vs. non-stationarity, ARIMA and ARIMAX Modeling

Case Study

Title: Optimization in Analytics

Duration (Hours): 10

Introduction to Operations Research (OR), Linear Programming Problems (LPP), Geometry of linear programming, Sensitivity and Post-optimal analysis, Duality and its economic interpretation.

Non-linear Programming – KKT conditions, Quadratic Programming, Portfolio optimization.

Title: Introduction to SQL and Business Intelligence

Duration (Hours): 5

Learning SQL query structure with examples, Data management and query system OLTP and OLAP and Their data models, Data warehousing, ETL and data integration

Title: Excel, Tableau and Business Intelligence

Duration (Hours): 15

Excel for Analytics: Data Cleaning and Processing, Vlookup, Pivot table and Dashboards, Charts, Date functions, Conditional Formatting and Data Validation, VBA, Dynamic Arrays and lambda

Tableau and Business Intelligence: Dashboard creation using Tableau, Concepts of Business intelligence (BI), the relevance of BI in application to analytics, industry   and different domains.