BD Analytics, BI & Cloud Computing

• Introduction to Data Analytics, BI, and Cloud Computing
• Introduction to Data Analytics, Business
• Intelligence (BI), and Cloud Computing.
• Understanding the difference between the roles of Data
• Analyst, BI Analyst & Data Scientist in decision-making.
• Understanding the challenges lie ahead in developing real-time refreshing BI solutions for large scale data and how
  cloud computing come as a rescue.
• Overview of popular Data Analytics Tools and Technologies.

Statistics – I

• Statistics: Statistics is used in almost any field; What concepts you only require doing your job successfully as a Data
  Analyst and Data Scientist?
• Types of Data, Variable, and Frequency Distribution.
• Plots you need to know about and in which situation you need to use which one, Use cases.

Statistics – II

• Frequency Distribution, Measures of Central Tendency [Mean, Median, Mode], and Location [Quartile, Decile,
  Percentile].
• Measures of Dispersion [Range, Standard Deviation, Variance, Coefficient of Variation].
• Measures of Shape Characteristics: Skewness, Kurtosis.

Statistics – III

• Standardization of Data, Z-score.
  Best way to Detect Outlier and Impute Missing Value.
• Time Series Analysis Concepts.
• Sampling Methods and Sampling Size Determination.
• Cross Sectional Study, Longitudinal Study, Panel and Time Series Study.

Statistics – IV

• Correlation, Linear Regression, Logistic Regression, ANOVA; When to Perform which one?
• Hypothesis Testing use cases and when to pick which one?
• Concept of A/B Testing
• P-value; Probability, Prior Probability, Posterior Probability

Microsoft Excel – I

• Excel Important Functions: vlookup, xlookup, Datediff, if, ifs, sumif, countif, etc.
• Filter, Unique, Removing Duplicates, Number Formatting
• Conditional Formatting.

Microsoft Excel – II

• Statistical Data Analysis using Excel.

Microsoft Excel – III

• Web Scrapping in Microsoft Excel
• Data Cleaning, Data Consolidation & Automizing Work with Power Query

Microsoft Excel – IV

• Summarizing & Report Building using Pivot Table
• Building Interactive Charts
• Attractive Looking Dashboard Building using Microsoft Excel

End-to-End Data Analysis Project using Microsoft Excel

• Cloud Architecture to work with Big Data
• Cloud-based Data Storage and Processing: Introduction to cloud Databases, Data Warehousing, and Lake houses.
• Difference between Database, Data Warehouse, and Lakehouse.
• Difference between OLTP and OLAP.
• Knowing Azure Synapsis Platform, Microsoft Fabric, Snowflake.

Introduction to Power BI-I

• Leveraging Cloud Computing for scalable BI & Data Analytics Solutions.
• Getting Familiar with the Power BI User Interface, Features, Settings, Strength & Weakness.

Power BI – II

• Data Modelling in Power BI [Import & Direct Query Mode]
• Advantages and Disadvantages between these two methods.
• Data Cleaning & New Measures in Power BI.

Power BI – III

DAX Measures in Power BI.

Taking help from Copilot, an AI Feature of Microsoft to get it all done.

Power BI – IV

• Getting familiar with different Charts & Reporting Tables
• Formatting of the Charts and Reports

Power BI – V

• AI Powered Charts & Features
• Interpretation of the Findings

Power BI – VI

• Reports and Dashboard Building using Power BI
• Understanding different features and build aesthetically looking professional standard Dashboards.

Power BI – VII

• More on Reports and Dashboard Building using Power BI
• Adding Bookmarks, Tooltips, and Navigation to Different Page of the Reports

Power BI – VIII

• Power BI Service UI & Features
• Hosting Dashboard/Report using Power BI Service, Sharing with Other and Maintaining all the Securities.

Power BI – IX

• Working on Azure Synapsis
• Discussing the optimization of Cloud Computing Cost
• Connecting Lakehouse, Serverless SQL Pool & Power BI
• Working with Delta Format Data, Partitioning, and Hybrid Table in Power BI

Power BI – X

• How to work with Big Data and Build real-time refreshing Dashboard.
• Working with Direct Query Method
• Building data pipelines and processing large datasets in the cloud.
• Ensuring data accuracy, consistency, and integrity in cloud-based analytics.

Power BI – XI

• Recap of Everything we have learned so far.

• Building Realtime Dashboard using Power BI and Azure Synapsis Cloud Computing Platform

Fundamentals of DBMS – I

• What is Relational Database Management System?
• ACID Property
• How is data stored in a Relational Database?
• Concept of Normalization.
• What is NoSQL and BASE Property?

Fundamentals of DBMS – II

• Difference Between Relational and NoSQL Database.
• Which one you should choose in which case?
• What is Snowflake & What advantage does it provide?
• Concepts of Micro-Partitioning and Columnar Storage.
• Creating Trial Account in Snowflake.
• Snowflake UI Tour.

Fundamentals of DBMS – III

• Installing MySQL Database & MySQL Workbench
• Setting up another IDE to work with any Database; DBeaver

Fundamentals of DBMS – IV

• Concepts of Database, Schema, Table and Fields.
• Types of SQL Commands: DDL, DML, DCL, TCL, DQL.
• What are the Commands under each of these categories and what each of these commands do?
• As a Data Analyst & Data Scientist which Commands you only need to Master?
• Constraints: Primary Key, Foreign Key, Not Null, Unique, Check, Default.
• Data Types: DATE, DATETIME, VARCHAR, INT, NUMBER, FLOAT, Auto Increment.

MySQL & Snowflake – I

• Designing and Creating a Database, Schema, Tables
• Masterclass on DDL, DML, TCL & DQL Commands/Statements

MySQL & Snowflake – II

• Creating File Format to Bulk Insert Data
• Bulk Insert of Data in Snowflake & MySQL
• Error Handling during Bulk Insertion

MySQL & Snowflake – III

• Basic Queries: SELECT, FROM, WHERE, LIKE, ILIKE, IN, DISTINCT, BETWEEN, GROUP BY, ORDER BY, LIMIT, OFFSET, ALIAS.
• Aggregate Functions: COUNT, SUM, AVG, MIN, MAX.
• Difference between WHERE and HAVING Clause.

MySQL & Snowflake – IV

• SQL Join: Left, Right, Inner, Full, Cross Join
• UNION, UNION ALL
• SQL Code Order of Execution
• CASE WHEN STATEMENT AND WIDE USE OF THIS DURING DATA CLEANING, ANALYSIS AND FEATURE ENGINEERING

MySQL & Snowflake – V

• Sub Queries in SELECT, FROM and WHERE Clause
• Common Table Expressions (CTE)
• Between Sub Queries and CTE, which one is more efficient and takes less time?

MySQL & Snowflake – VI

• Window Functions, the most widely used SQL Commands used by Data Analysts
• Window Functions: RANK, DENSE_RANK, ROW_NUMBER, LEAD, LAG, FIRST VALUE, AGGREGATE WINDOW FUNCTION,
  FRAME SPECIFICATION, WINDOW CHAINING

MySQL & Snowflake – VII

• Some built-in Functions: EXTRACT, DATE_PART, TO_DATE, TO_CHAR
• CASTING, SUBSTRING, POSITION, COALESCE, NULLIF

MySQL & Snowflake – VIII

• VIEW, MATERIALIZED VIEW, AND THE DIFFERENCE BETWEEN THESE
• Recap and Review of key concepts.
• End-to-end Project Database Creation, Data Insertion, Data Cleaning & Data Analytics Project

Solution of the End-to-end Project Database Creation, Data Insertion, Data Cleaning & Data Analytics Project.

By following this training plan, participants will gain a comprehensive understanding of Statistical Methodologies, Data Analytics,
Modern Business Intelligence, and Cloud Computing concepts. They will be equipped with the necessary skills to apply these
technologies in real-world scenarios, enabling them to make data-driven decisions and leverage the power of the cloud for scalable
and efficient analytics.

Participants completing the projects will be a valuable asset to any company that wants to leverage datadriven decision in their growth.