Master's In Data Science With GenAi

a. Sum-if & ifs
b. Average-if & ifs
c. Count-if & ifs
d. Lookup Function - VLOOKUP, HLOOKUP, XLOOKUP
e. Excel Shortcuts

a. Sum-if & ifs
b. Average-if & ifs
c. Count-if & ifs
d. Lookup Function - VLOOKUP, HLOOKUP, XLOOKUP
e. Excel Shortcuts

a. Data Analysis
b. Correlation
c. Regression
d. Co-variance
e. Moving Average
f. Descriptive Stats
g. What-If Analysis
h. Goal Seek
I. Scenario Manager
j. Pivot Query
k. Loading Data
L. Transformations
m. Live Data Connection and Analysis.

a. Overview of Data
b. Types and Forms of Data
c. Applications and Examples of Data
d. Overview of Statistics
e. Types of Statistics - Applications and Examples

a. Overview of Descriptive Statistics
b. Functions in Descriptive Statistics - Applications and
Its Mathematical Explanation
      i. Mean
      ii. Median
      iii. Mode
c. Standard Deviation
d. Skewness
e. Kurtosis
f. Range
g. Variance
h. Co-efficient Of Variation
I. Correlation
      i. Pearson
      ii. Spearman
j. Co-variance
k. Squared Error
L. Mean Squared Error, RMSE, MAD
m. Hands On Python/Excel

a. Overview of Inferential Statistics
b. Population and Sample
c. Test in Inferential Statistics
d. T-test
e. Chi-square
f. Anova
g. P-value
h. Tables and Critical value
I. Confidence Interval
j. Hands on in Python/Excel

a. What is Probability Distribution?
b. Examples and Applications of Probability Distribution
c. What are Normal Distributions?
d. What are Uniform Distributions?
e. What are Skewed Distributions?
f. What is a z-score?
g. Hands on in Python/Excel

a. Why we need Programming in Data Science
b. In which language can we do Programming?
c. Overview of Programming
d. What is Compiler and Interpreter?

a. Python Overview
b. Python Installations
c. Setting paths in Python
d. Coding with IDLE
e. Installing Editor - PyCharm/VS-Code
f. Setting interpreter

a. What is Anaconda?
b. Installations
c. Keywords and Identifiers in Python
d. Comments, Indentations and Statements

a. Integers
b. Strings
c. Float
d. Boolean
e. Bytes

a. Arithmetic Operators
b. Logical Operators
c. Membership operators
d. Equality Operators
e. Comparison Operators

a. If
b. If...... elseif
c. Loops -
d. For Loop
f. While Loop
g. Transfer Statements
h. Break
I. Continue.
j. Pass

a. Lists
b. Tuple
c. Sets
d. Dictionary
e. Range

a. Python Inbuilt Functions
b. Lambda Functions
c. User Defined Functions
d. Arguments in Functions

e. Modules in Python-
f. Math Module

g. Random Module
h. Exception Handling in Python

a. Overview of Pandas
b. Pandas Functions
c. Forms in Pandas
d. Data Frame Basics
e. Key Operations in Data Frame and Series
f. Data Analysis using Pandas

a. Introduction to NumPy
b. Introduction to Array
c. Numerical operations

a. Data Cleaning
b. Missing Values Handling
c. Handling categorical and Numerical Features
d. Outlier Detection and Imputation
e. Exploratory Data Analysis (EDA)
f. Decide Suitable Algorithms
g. Standard Scaler, Normalization, transformations

a. Introduction to matplotlib
b. Introduction to Seaborn
c. Introduction to Iris dataset and 2D scatterplot.
d. 3D scatterplot.
e. Pair plots.
f. Limitations for Pair plots.
g. Histogram and introduction to PDF (Probability Density Function).
h. Univariate analysis using PDF.
I. CDF (Cumulative Distribution Function)
j. Variance, Standard Deviation.
k. Median.
L. Percentiles and Quartiles.
m. IQR (Interquartile Range) Boxplot with whiskers
n. Violin plots.
      i. Heatmaps and Correlations Plots
      ii. Regression Plots
      iii. Line Plots
      iv. Pie Charts and Donut Charts in Python

a. Introduction to databases.
b. Why SQL?
c. Installing MySQL.
d. Load Data.
e. Use, Describe, Show Table.
f. Select

g. Order By, Distinct.
h. Where Clause, Comparison Operators, NULL.
I. Logic Operators
j. Aggregate Functions: COUNT, MIN, MAX, AVG, SU
k. Group By.
L. Join and Natural Join.
m. Subqueries/Nested Queries/Inner Queries.
n. DML: INSERT.
o. DML: UPDATE, DELETE.
p. DML: CREATE, TABLE.
q. DDL: DROP TABLE, TRUNCATE, DELETE.
r. Normalisation
s. Dimensional Modelling
t. Window Functions
      i. SUM
      ii. MAX
      iii. Average
      iv. Rank
       v. Partition
      vi. Order by
u. Views, Stored Procedures and Tables in MYSQL
v. Data Analysis and Data Wrangling In MYSQL
w. Temporary table creation
x. Database Administration

a. Introduction to Power BI - Need, Importance
b. Power BI - Advantages and Scalable Options
c. History - Power View, Power Query, Power Pivot
d. Power BI Architecture and Data Access
e. Power BI Desktop - Installation, Usage
f. Sample Reports and Visualization Controls
g. Understanding Desktop & Mobile Editions
h. Report Rendering Options and End User Access

a. Report Design with Legacy & .DAT Files
b. Report Design with Database Tables
c. Understanding Power BI Report Designer
d. Report Canvas, Report Pages: Creation, Renames
e. Report Visuals, Fields and UI Options
f. Experimenting Visual Interactions, Advantages
g. Reports with Multiple Pages and Advantages
h. Pages with Multiple Visualizations. Data Access
I. "GET DATA" Options and Report Fields, Filters
j. Report View Options: Full, Fit Page, Width Scale
k. Report Design using Databases & Queries
L. Query Settings and Data Preloads
m. Navigation Options and Report Refresh
n. Stacked bar chart, Stacked column chart
o. Clustered bar chart, Clustered column chart
p. Adding Report Titles. Report Format Options
q. Focus Mode, Explore and Export Settings

a. Power BI Design: Canvas, Visualizations and Fields
b. Import Data Options with Power BI Model, Advantages
c. Direct Query Options and Real-time (LIVE) Data Access
d. Data Fields and Filters with Visualizations
e. Visualization Filters, Page Filters, Report Filters
f. Conditional Filters and Clearing. Testing Sets
g. Creating Customised Tables with Power BI Editor
h. General Properties, Sizing, Dimensions, and Positions
I. Alternate Text and Tiles. Header (Column, Row) Properties
j. Grid Properties (Vertical, Horizontal) and Styles
k. Table Styles & Alternate Row Colors - Static, Dynamic
L. Sparse, Flashy Rows, Condensed Table Reports. Focus Mode
m. Totals Computations, Background. Borders Properties
n. Column Headers, Column Formatting, Value Properties
o. Conditional Formatting Options - Colour Scale
p. Page Level Filters and Report Level Filters
q. Visual-Level Filters and Format Options
r. Report Fields, Formats and Analytics
s. Page-Level Filters and Column Formatting, Filters
t. Background Properties, Borders and Lock Aspect

a. Chart report types and properties
b. Stacked bar chart, stacked column chart
c. Clustered bar chart, clustered column chart
d. 100% stacked bar chart, 100% stacked column chart
e. Line charts, area charts, stacked area charts
f. Line and stacked row charts
g. Line and stacked column charts
h. Waterfall chart, scatter chart, pie chart
I. Field Properties: Axis, Legend, Value, Tooltip
j. Field Properties: Colour Saturation, Filter Types
k. Formats: Legend, Axis, Data Labels, Plot Area
L. Data Labels: Visibility, Colour and Display Units
m. Data Labels: Precision, Position, Text Options
n. Analytics: Constant Line, Position, Labels
o. Working with Waterfall Charts and Default Values
p. Modifying Legends and Visual Filters - Options
q. Map Reports: Working with Map Reports
r. Hierarchies: Grouping Multiple Report Fields
s. Hierarchy Levels and Usages in Visualizations
t. Preordered Attribute Collection - Advantages
u. Using Field Hierarchies with Chart Reports
v. Direct Import and In-memory Loads, Advantages

a. Hierarchies and Drilldown Options
b. Hierarchy Levels and Drill Modes - Usage
c. Drill-thru Options with Tree Map and Pie Chart
d. Higher Levels and Next Level Navigation Options
e. Aggregates with Bottom/Up Navigations. Rules
f. Multi Field Aggregations and Hierarchies in Power BI
g. DRILLDOWN, SHOWNEXTLEVEL, EXPANDTONEXTLEVEL
h. SEE DATA and SEE RECORDS Options. Differences
I. Toggle Options with Tabular Data. Filters
j. Drilldown Buttons and Mouse Hover Options @ Visuals
k. Dependant Aggregations, Independent Aggregations
L. Automated Records Selection with Tabular Data
m. Report Parameters: Creation and Data Type
n. Available Values and Default values. Member Values
o. Parameters for Column Data and Table / Query Filters
p. Parameters Creation - Query Mode, UI Option
q. Linking Parameters to Query Columns - Options
r. Edit Query Options and Parameter Manage Entries
s. Connection Parameters and Dynamic Data Sources

a. Understanding Power Query Editor - Options
b. Power BI Interface and Query / Dataset Edits
c. Working with Empty Tables and Load / Edits
d. Empty Table Names and Header Row Promotions
e. Undo Headers Options. Blank Columns Detection
f. Data Imports and Query Marking in Query Editor
g. JSON Files & Binary Formats with Power Query

h. JavaScript Object Notation - Usage with M Lang.
I. Applied Steps and Usage Options. Revert Options
j. Creating Query Groups and Query References. Usage
k. Query Rename, Load Enable and Data Refresh Options
L. Combine Queries - Merge Join and Anti-Join Options
m. Combine Queries - Union and Union All as New Dataset
n. M Language: Nested Join and Join Kind Functions
o. REPLACE, REMOVE ROWS, REMOVE COL, BLANK - M Lang
p. Column Splits and Filled Up / Filled Down Options
q. Query Hide and Change Type Options. Code Generation

a. Purpose of Data Analysis Expressions (DAX)
b. Scope of Usage with DAX. Usability Options
c. DAX Context: Row Context and Filter Context
d. DAX Entities: Calculated Columns and Measures
e. DAX Data Types: Numeric, Boolean, Variant, Currency
f. Datetime Data Tye with DAX. Comparison with Excel
g. DAX Operators & Symbols. Usage. Operator Priority
h. Parenthesis, Comparison, Arithmetic, Text, Logic
I. DAX Functions and Types: Table Valued Functions
j. Filter, Aggregation and Time Intelligence Functions
k. Information Functions, Logical, Parent-Child Functions
L. Statistical and Text Functions. Formulas and Queries
m. Syntax Requirements with DAX. Differences with Excel
n. Naming Conventions and DAX Format Representation
o. Working with Special Characters in Table Names
p. Attribute / Column Scope with DAX - Examples
q. Measure / Column Scope with DAX – Examples

a. YTD, QTD, MTD Calculations with DAX
b. DAX Calculations and Measures
c. Using TOPN, RANKX, RANK.EQ
d. Computations using STDEV & VAR
e. SAMPLE Function, COUNTALL, ISERROR
f. ISTEXT, DATEFORMAT, TIMEFORMAT
g. Time Intelligence Functions with DAX
h. Data Analysis Expressions and Functions
i. DATESYTD, DATESQTD, DATESMTD
j. ENDOFYEAR, ENDOFQUARTER,ENDOFMONTH
k. FIRSTDATE, LASTDATE, DATESBETWEEN
l. CLOSINGBALANCEYEAR,CLOSINGBALANCEQTR
m. SAMEPERIOD and PREVIOUSMONTH,QUARTER
n. IF..ELSEIF.. Conditions with DAX
o. Slicing and Dicing Options with Columns, Measures
p. DAX for Query Extraction, Data Mashup Operations
q. Calculated Columns and Calculated Measures with DAX

a. PowerBI Report Validation and Publish
b. Understanding PowerBI Cloud Architecture
c. Data Refresh with Power BI Architecture
d. PBIX and PBIT Files with Power BI - Usage
e. Visual Data Imports and Visual Schemas

a. Relationships
b. Joins and Cardinality
c. Snowflake Schema
d. Star Schema in Power BI

a. Tokenization
b. Data Cleaning – Trimming, Case Conversion, Imputations, Removing.

a. What are LLMs?
b. Evolution of Natural Language Processing
c. How does an LLM outputs a word?
d. Next Word Prediction
e. Training and using LLMs
f. Ways to use LLMs: - Text Response and Embeddings
g. LLM Embeddings
h. Sentiment Analysis with LLMs
I. Serendipity in LLMs
j. How are LLMs revolutionizing industries today?

a. Study of GPT architecture and variants.
b. Applications of GPT models in text generation and dialogue systems.
c. Case study-based implementation of GPT-based tasks. GPT-based
chatbot enhances E-Shop's customer support service or any other use case.

a. Introduction to Vector Databases
b. Architecture of Vector Databases
c. Indexing Techniques
d. Distance Metrics and Similarity Measures
e. Nearest Neighbour Search
f. Open-Source Vector Databases: - Chroma and Milvus

a. Attention Mechanism
b. Transformer Architecture and components
c. GPT Architecture
d. GPT Training Process: - Pre-Training and Fine-Tuning
e. Building your own GPT from scratch using Open-Source API’s

a. Art of Prompt Engineering

a. Fine Tuning
b. Prompt Engineering
c. Text Summarizations.
d. Text Generation

a. Data Science and It's Concept
b. Scope of Data Science
c. Data Engineering
d. Data Stories
e. Business Intelligence and It's Concepts
f. Application of Bi in The Real World
g. Basics of Artificial Intelligence
h. Applications of Al in The Real World

a. Definition/Overview
b. Importance of Data Science
c. Role and Scope of Data Science
d. History and Future of Data Science
e. Benefits and Challenges of Data Science
f. Life Cycle of Data Science
g. Salaries of Data Science Roles

a. Definition/Overview
b. Concept and Tools Of B!?
c. Life Cycle of Business Intelligence

a. Definition/overview
b. Examples
c. Applications of Data Mining

a. What is ML?
b. Different types of ML?
c. Life Cycle of ML?
d. Challenges of ML?

a. Linear Regression Analysis
      i. Geometric intuition of Linear Regression
      ii. Geometric intuition of Multiple Linear Regression
      iii. Lasso and Ridge Regression
      iv. Polynomial Regression
       v. Diagnostics of Regression
      vi. Rsquare
     vii. RMSE, MSE
    viii. Sampling
         • Train-Test Split
         • Stratified Sampling
         • Cross validation

b. Classification Analysis
      i. Geometric intuition of Logistic Regression
      ii. Imbalanced vs Balanced Datasets Analysis
      iii. Diagnostics of Classifications -
      iv. Accuracy
       v. Confusion Matrix
      vi. TPR, FPR, FNR, TNR. 4. Precision and recall, Fl-score.
      vii. Receiver Operating Characteristic Curve (ROC) curve and AUC
c. k Nearest Neighbour Algorithm (KNN)
      i. Maths Behind KNN Distance Calculations in KNN
      ii. Elbow Method
d. Decision Tree
      i. Geometric intuition of decision tree.
      ii. Sample Decision tree.
     iii. Building a Decision Tree: Entropy (Intuition
      vi. Building a Decision Tree Information gain.
       v. Building a Decision Tree: Gini Impurity.
e. Random Forest
      i. Geometric Intuition of Random Forest
     ii. Feature Importance
     iii. Predicted Probabilities

a. Clustering-
       i. Overview of Clustering
      ii. Applications of Clustering
      iii. K-Means Clustering Analysis
          • K-Means: Geometric Intuition,
          • Centroids
          • K-Means: Mathematical formulation: objective function
          • K-Means Algorithm.
          • Failure cases/ Limitations.
          • Determining the right K-Elbow method.
      iv. Hierarchical Clustering Analysis
          • Agglomerative & Divisive, Dendrograms.
          • Agglomerative clustering.
          • Proximity methods: Advantages and Limitations.

a. What is NLP?
b. What is Text Mining?
c. Text Mining Process
d. Punctuation Remover
e. Deletion, Cleaner
f. Stop Words Remover
g. Lemmatize
h. Stemmer
I. POS tagging
j. Tokenization
k. Word Cloud
L. Sentiment Detection

a. What is Deep Learning?
b. Scope and Challenges
c. Life Cycle
d. Epochs
e. Real-Life use cases of Deep Learning
f. Single Layer Neural Network
g. Multi-Layer Neural Network
h. Important Python packages for Deep Learning
I. Activations Functions

a. Introduction to Artificial Intelligence (AI)
b. Modern era of AI
c. Role of Machine learning & Deep Learning in AI
d. Hardware for AI (CPU vs. GPU vs. FPGA)
e. Software Frameworks for AI & Deep Learning
f. Key Industry applications of AI
g. What is Cloud Computing? Why it matters?
h. Traditional IT Infrastructure vs. Cloud Infrastructure
i. Cloud Companies (Microsoft Azure, GCP, AWS) & their Cloud
Services (Compute, storage, networking, apps, cognitive etc.)
j. Use Cases of Cloud computing
k. Overview of Cloud Segments: IaaS, PaaS, SaaS
l. Overview of Cloud Deployment Models
m. Overview of Cloud Security
n. AWS vs. Azure vs. GCP

a. Overview of Streamlit
b. Application of Streamlit and its Uses
c. Streamlit Simple App Creation in Python
d. Machine Learning in Streamlit

a. Model Serialization (Json, XML)
b. Updatable Classifiers
c. Batch mode.
d. Joblib, Pickle

a. Profile Creation
b. Repository Creation
c. Maintenance
d. Tags in GitHub
e. Uploading Files in GitHub
f. Commit Changes

a. What is SDLC?
b. Different methods in SDLC.
c. What is difference between Waterfall and Agile?
d. What are the advantages and disadvantages of Agile?
e. What are the advantages and disadvantages of Waterfall?
f. Jira and its Uses