Session 1:
- 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.
Session 2:
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.
Session 3:
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.
Session 4:
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.
Session 5:
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
Session 6:
Microsoft Excel – I
- Excel Important Functions: vlookup, xlookup, Datediff, if, ifs, sumif, countif, etc.
- Filter, Unique, Removing Duplicates, Number Formatting
- Conditional Formatting.
Session 7:
Microsoft Excel – II
- Statistical Data Analysis using Excel.
Session 8:
Microsoft Excel – III
- Web Scrapping in Microsoft Excel
- Data Cleaning, Data Consolidation & Automizing Work with Power Query
Session 9:
Microsoft Excel – IV
- Summarizing & Report Building using Pivot Table
- Building Interactive Charts
- Attractive Looking Dashboard Building using Microsoft Excel
Session 10:
End-to-End Data Analysis Project using Microsoft Excel
Session 11:
- 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.
Session 12:
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.
Session 13:
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.
Session 14:
Power BI – III
DAX Measures in Power BI.
Taking help from Copilot, an AI Feature of Microsoft to get it all done.
Session 15:
Power BI – IV
- Getting familiar with different Charts & Reporting Tables
- Formatting of the Charts and Reports
Session 16:
Power BI – V
- AI Powered Charts & Features
- Interpretation of the Findings
Session 17:
Power BI – VI
- Reports and Dashboard Building using Power BI
- Understanding different features and build aesthetically looking professional standard Dashboards.
Session 18:
Power BI – VII
- More on Reports and Dashboard Building using Power BI
- Adding Bookmarks, Tooltips, and Navigation to Different Page of the Reports
Session 19:
Power BI – VIII
- Power BI Service UI & Features
- Hosting Dashboard/Report using Power BI Service, Sharing with Other and Maintaining all the Securities.
Session 20:
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
Session 21:
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.
Session 22:
Power BI – XI
- Recap of Everything we have learned so far.
Session 23:
- Building Realtime Dashboard using Power BI and Azure Synapsis Cloud Computing Platform
Session 24:
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?
Session 25:
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.
Session 26:
Fundamentals of DBMS – III
- Installing MySQL Database & MySQL Workbench
- Setting up another IDE to work with any Database; DBeaver
Session 27:
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.
Session 28:
MySQL & Snowflake – I
- Designing and Creating a Database, Schema, Tables
- Masterclass on DDL, DML, TCL & DQL Commands/Statements
Session 29:
MySQL & Snowflake – II
- Creating File Format to Bulk Insert Data
- Bulk Insert of Data in Snowflake & MySQL
- Error Handling during Bulk Insertion
Session 30:
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.
Session 31:
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
Session 32:
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?
Session 33:
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
Session 34:
MySQL & Snowflake – VII
- Some built-in Functions: EXTRACT, DATE_PART, TO_DATE, TO_CHAR
- CASTING, SUBSTRING, POSITION, COALESCE, NULLIF
Session 35:
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
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.
