Advanced Certification in Data Analytics for Business

1. Introduction to SQL
2. Database Normalization and Entity Relationship Model(self-paced)
3. SQL Operators
4. Working with SQL: Join, Tables, and Variables
5. Deep Dive into SQL Functions
6. Working with Subqueries
7. SQL Views, Functions, and Stored Procedures
8. Deep Dive into User-defined Functions
9. SQL Optimization and Performance
10. Advanced Topics
11. Managing Database Concurrency
12. Practice Session

Case Study

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Excel Fundamentals

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

Excel For Data Analytics

1. 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

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

Ensuring Data and File Security

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

Getting started with VBA Macros

1. 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

1. 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.

Descriptive Statistics

1. Measure of central tendency, measure of spread, five points summary, etc.

Probability

1. Probability Distributions, Probability in Business Analytics
2. Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem.

Inferential Statistics

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

Case Study

This case study will cover the following concepts:

1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data

Introduction to Machine learning

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

Regression

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

Classification

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

Clustering

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

Supervised Learning

1. Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
2. 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

1. K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
2. Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
3. Linear Discriminant Analysis – LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
4. Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

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

Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

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

1. Finance
2. Marketing
3. Retail
4. 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:

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

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:
This case study will cover the following concepts:

• 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.

Introduction to Data Warehouse – Introducing Data Warehouse and Business Intelligence, understanding difference between database and data warehouse, working with ETL tools, SQL parsing.

Architecture of Data Warehouse – Understanding the Data Warehousing Architecture, system used for Reporting and Business Intelligence, understanding OLAP vs. OLTP, introduction to Cubes.

Data Modeling Concepts – The various stages from Conceptual Model, Logical Model to Physical Schema, Understanding the Cubes, benefits of Cube, working with OLAP multidimensional Cube, creating Report using a Cube.

Data Normalization – Understanding the process of Data Normalization, rules of normalization for first, second and third normal, BCNF, deploying Erwin for generating SQL scripts.

Dimension and Fact Table – The main components of Business Intelligence – Dimensions and Fact Tables, understanding the difference between Fact Tables & Dimensions, understanding Slowly Changing Dimensions in Data Warehousing.

SQL Parsing, Cubes and OLAP – SQL parsing, compilation and optimization, understanding types and scope of cubes, Data Warehousing Vs. Cubes, limitations of Cubes and evolution of in-memory analytics.

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.

Introduction to Presto – Learn about presto, what it is used for, and how the distributed SQL query engine works towards analytics and big data.

Data Transformation using Presto –

• Combined ETL and AI workloads.
• Querying Data from the source, etc.

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.

Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.

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.)

Census – After studying the population data, we will gather insights and through predictive modeling try to create actionable insights on the same, it could be average income of an individual, or most likely profession, etc.

Predictive Modeling – Various case studies on categorical and continuous data, to create predictive models that will predict specific outcomes based on the business problems.

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.

Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.

Housing – A case study that will give you insight into how real estate firms can narrow down on the pricing, customer choices, etc. using various predictive modeling techniques.

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.