Day 1 : Data Science, Google Looker Studio

• Sources of data, need for analysis and insights

• Data scientist vs data analyst vs business analyst

• Data science tools: SQL, Python, Power BI, Tableau, Excel

• Google Looker Studio, loading data and creating dashboards

Day 2 : Exploratory Data Analysis Using Python

• EDA need, Python benefits - ease, versatility, & libraries

• Dataset loading in Kaggle, Python basics - keywords, identifiers, variables, data types, operators, statements

• Stock market share data loading, read operations, nested loop operation

• EDA on Zomato restaurant dataset, set detail operations, basic data cleaning, extraction, visualization examples, data export

• EDA on Shark Tank dataset, read and extraction operations, creating interactive visualization using plotly

Day 5 : Tableau Publishing & Advanced Charts

• Tableau public dashboard publishing

• Dual-axis chart creation, style formatting

• Building a map, colour by count formatting

Day 1 : Power BI, Power Query

• Power BI features and use cases

• Data transformation using Excel, formula generation using ChatGPT, adding/editing data

• Practical - cards, time series, anomaly setting, tree map, gauge chart, target setting, adding calculations, area chart, decomposition tree, filter, bar chart, slicer, drill down analysis, smart narrative

• Power Query preview, edit/add data & calculations 

Day 1 : Data Preparation & Tableau

• Data cleaning in Excel, formula generation using ChatGPT

• Tableau public interface, worksheets & dashboard creation overview

• Practical - tree map, bar chart, and time series analysis charts & combined interactive dashboard creation for Amazon sales dataset

• Professional dashboard example with advanced dashboards, AI powered insights, data export, etc.

Day 2 : SQL Language

• Structured Query Language (SQL) and relational databases (two-dimensional tables) introduction

• Create, Read, Update & Delete (CRUD) operations on a database

• Tutorials on SQLBolt website with series of interactive lessons and exercises

• Selection, filtering & sorting queries on Pixar's classic movies & North American cities datasets

Day 1 : Power BI Deep Dive

• Business intelligence & data visualization basics

• Power BI interface walkthrough, data loading

• Practical - line/bar graph, tree map, pie/donut chart, tabular data, cards, slicers creation on interactive dashboard for Walmart sales dataset

• Dashboard formatting, textual insights, Q&A section

• Advanced dashboards - forecasts, what-if analysis, key influences, decomposition/drilldown

Day 2 : SQL for Datasets & Tableau

• Data import, export in SQLite online

• Selection, filtering & sorting queries on Amazon sales & product returns datasets

• Calculations and Join functions on data from both sets

• Creating Tableau dashboard for getting insights visually from operated data

Day 1 : Data Science, Analytics, & Power BI

• What is data science, it's importance, & key skills

• Lifecycle: collection, processing, EDA, model building, deployment, & monitoring

• Importance of data visualization, types, & tools

• Coffee chain dataset analysis dashboard in Power BI

Day 2 : Data Vizualization using Python

• Data science jobs vs salary dataset analysis in Power BI

• Smartphone weight vs battery size dataset exploration usnig Python in Google Colaboratory

• Data upload and scatter plot graph with formatting

• Additional real-world data analytics & data science skills, data storytelling 

Day 1 : Data Science & Power BI

• Types of data: qualitative vs quantitative, discrete & continuous

• Data science process: understanding business, data, preparation, modelling, evaluation, deployment

• Practical: Delta Fast, analyzing aircraft industry data

• Power BI, data loading, Power Query, creating measures

• Dashboards, column, line chart, card, treemap, map, pivot table, gauge, title

Day 2 : Predictive Modelling in Python

• Practical: Sunshine Reality, estimating median house prices 

• Developing a predictive model with Python in Jupyter

• Importing libraries, data loading, data frame splicing

• Supervised vs unsupervised learning, regression & classification

•  Scikit learn modelling, train_test_split, predict, LinearRegression, r2_score

Data Analysis, Power BI & Portfolios

• Power Platforms: BI, Apps, Automate & Pages

• Power BI vs Excel, Power BI vs Tableau vs Looker Studio

• Core analysis expectations & project lifecycle

• E-commerce sales data analysis project introduction

• Loading & exploring, Data section, Visualization section

• Filter section: Basic, Advanced & Top N

• Format section: visual & general, format painter

• Portfolio creation platforms, dataset sources

Power BI and Power Query

• Restaurant food ordering & superstore sales data explanation and insights

• Data sources, loading and transforming data

• Tableview DAX functions, transformations using Power Query

• Interactive dashboard creation, deciding metrics to compare, grouping

• Adding multiple data sheets, interlinking, table tools

• Job search techniques using Naukri, Ambition Box & Google Trends

Excel, Power Query, data cleaning & charts

• Food delivery services' restaurant data explanation

• Data loading, formatting

• Custom & conditional column creation in Power Query

• Pivot table creation, value field settings