Executive Post Graduate Certification in Data Analytics

Excel Fundamentals

• Reading the Data, Referencing in formulas , Name Range, Logical
• Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
• Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
• VBA Macros, Ranges and Worksheet in VBA
• IF conditions, loops, Debugging, etc.

Excel For Data Analytics

• Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

• Charts, Pie charts, Scatter and bubble charts
• Bar charts, Column charts, Line charts, Maps
• Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Ensuring Data and File Security

• Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.

Getting started with VBA Macros

• Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.

Statistics with Excel

• ONE TAILED TEST AND TWO TAILED T-TEST, LINEAR REGRESSION,PERFORMING STATISTICAL ANALYSIS USING EXCEL, IMPLEMENTING LINEAR REGRESSION WITH EXCEL

Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc.

Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e. machine learning models, etc.

Case Study – The culmination of all the above concepts with real-world problem statements for better understanding.

Statistics and Descriptive Analytics using MS Excel

• Measure of central tendency, measure of spread, five points summary, etc.
• Probability Distributions, Probability in Business Analytics
• Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem.

Python for Descriptive, Diagnostic, and Inferential Statistics

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Prescriptive Analytics

Introduction to Machine learning

• Supervised, Unsupervised learning.
• Introduction to scikit-learn, Keras, etc.

Regression

• Introduction classification problems, Identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimize the efficiency of the regression model.

Classification

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model.

Clustering

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.

Supervised Learning

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Unsupervised Learning

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.

• Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.

• Regression and Multivariate Analysis 
• Classification problems in machine learning 
• Data Multidimensionality and Linear Algebra
• Feature engineering and Feature selection 
• Hyperparameter Tuning and other optimization techniques

• Understanding Natural Language Processing Applications (e.g. Search Engines and Social Media)
• Web Analytics (Google)
• Machine Learning Applications and Chatbots
• Social Media Analytics Advanced Text Mining like Sentiment Analysis, Topic Modelling, and Text Summarisation

Business Domains – Learn about various business domains and understand how one differs from the other.

• Finance
• Marketing
• Retail
• Supply Chain

Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations.

Exploratory Data Analysis to Gather insights – Learn about the exploratory data analysis and how it enables a fool proof producer of actionable insights.

Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in simple relatable format that makes it easier to understand and recall.

Case Study
This case study will cover the following concepts:

• Create actionable insights from raw unstructured data to solve real world business problems.

KNIME

• Introduction to KNIME – learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc.
• Working with data in KNIME – Learn about creating workflows, loading datasets in KNIME, etc.
• Loops in KNIME – Learn about the loops in KNIME that enables efficient data transformation in KNIME
• Web scraping in KNIME – Learn about techniques in KNIME that enable web scraping to collect data directly from the web.
• Feature Selection, Hyperparameter optimization in KNIME – Learn about hyperparameter optimization, feature selection in KNIME that will enable efficient machine learning models.
• Case Study: Using KNIME to create end to end machine learning models with various algorithms like linear regression, logistic regression, decision tree, random forest, etc.

Feature Selection – Feature selection techniques in python that includes recursive feature elimination, Recursive feature elimination using cross validation, variance threshold, etc.

Feature Engineering – Feature engineering techniques that help in reducing the best features to use for data modeling.

Model Tuning – Optimization techniques like hyperparameter tuning to increase the efficiency of the machine learning models.

Power BI Basics

• Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
• SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
• Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX

• Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

• Slicers, filters, Drill Down Reports
• Power BI Query, Q & A and Data Insights
• Power BI Settings, Administration and Direct Connectivity
• Embedded Power BI API and Power BI Mobile
• Power BI Advance and Power BI Premium

Case Study:

This case study will cover the following concepts:

• Creating a dashboard to depict actionable insights in sales data.

Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement.

Approach for the Solution – Creating various statistical insights based solutions to approach the problem will guide your learnings to finish a project from scratch.

Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements.

Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any.

Gathering Actionable insights – You will learn about how a problem’s solution isn’t just creating a machine learning model, the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.

Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.

Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.

Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.

Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next given time period(weeks, months, years, etc.)

HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.

Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.

Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.

Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in early detection of various diseases.

• Job Search Strategy
• Resume Building
• Linkedin Profile Creation
• Interview Preparation Sessions by Industry Experts
• Mock Interviews
• Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.

Introduction to Spark – Introduction to Spark, Spark overcomes the drawbacks of working on MapReduce, Understanding in-memory MapReduce, Interactive operations on MapReduce, Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision, The overview of Spark and how it is better than Hadoop, Deploying Spark without Hadoop, Spark history server and Cloudera distribution

Spark Basics – Spark installation guide, Spark configuration, Memory management, Executor memory vs. driver memory, Working with Spark Shell, The concept of resilient distributed datasets (RDD), Learning to do functional programming in Spark, The architecture of Spark.

Spark SQL and Data Frames

• Learning about Spark SQL
  
• The context of SQL in Spark for providing structured data processing
  
• JSON support in Spark SQL
  
• Working with XML data
  
• Parquet files
  
• Creating Hive context
  
• Writing data frame to Hive
  
• Reading JDBC files
  
• Understanding the data frames in Spark
  
• Creating Data Frames
  
• Manual inferring of schema
  
• Working with CSV files
• Reading JDBC tables
  
• Data frame to JDBC
  
• User-defined functions in Spark SQL
  
• Shared variables and accumulators
  
• Learning to query and transform data in data frames
  
• Data frame provides the benefit of both Spark RDD and Spark SQL
  
• Deploying Hive on Spark as the execution engine