From Beginner to PRO

Key Skills You’ll Gain in Adv. Data Science & Analytics with Gen-AI -
✓ Job-Focused Curriculum (Beginner to Advanced)
✓ Step-by-step learning from basics of Python to building advanced Real-world Projects in AI.
✓ Get hands-on on latest A.I. technologies like NLP, BIRT, LLM, Gen-AI, ML-OPS etc
more ...
100% Job Placement -
A structured framework designed to help you get job faster.
Job Opportunities -
Data Science Engineer, Data Scientist, Machine Learning Engineer, Data Analyst, Data Analytics Engineer etc.
Pre-requisites -
None. Any one from IT & non IT can learn.
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Syllabus

MySQL

Learn both MySQL & Adv. MySQL
MySQL & Database Basics
Installation
- Server
- Workbench (client)
DDL - Data Definition Language
- Tables - Create, Alter, Drop, Auto Increment
- Constraints - Not Null, Primary Key, Unique Key, Foreign Key
- Index - Why required, Create, Drop
DML - Data Manipulation Language
- Insert, Update, Delete & Truncate
Transactions (T SQL)
- Commit
- Rollback
DQL - Data Query Language (Select)
Filters
- Operators - <, <=, >, >=, =, !=
- BETWEEN
- IN
- LIKE
- NOT
- NULL
- AND, OR, NOT
- Multi conditions filtering
- DISTINCT
- Limit, offset
Sorting
- Ascending Order
- Descending Order
- Multi Column Sorting
Functions
- Data, number, characters, null values etc.
- Case
Groups
- Basics
- Grouping functions - AVG, MIN, MAX, COUNT, DISTINCT COUNT
- Grouping Filters - HAVING
Joins
- Cartesian Product
- Equi and Non-Equi Joins
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Self Join
SET Operators
- Union
- Union All
- Intersect
- Minus
Subquery
- Single Valued
- Multi Valued
Views & Inline Views
Procedures
- Programming basics - if..else, for loop
- Procedures - create, drop
Functions
- create, drop
Triggers
- create, drop
Analytic (Window) Functions
- Top N Analysis
- Over() with summary functions
- Partition By Queries
- ROW_NUMBER()
- RANK()
- DENSE_RANK()
Project

Python

Introduction & Installation
- Introdution
- Installing Python
- Installing Pycharm
- Configurations
Programming Basics
- Variables
- Data Types
- User Inputs
- Unpacking
- Boolean Practice
- What If, Else and If, Making Decisions
- For Loops
- Break Continue Pass
- Break Vs Continue Vs Pass
Data Structures
- List
- Tuple
- Set
Logic Building & Programming
- Pattern Programs
- Prime Number
- Fibonacci Sequence
- Factorial
- Recursion
- Swap 2 Variables
Functions
- Creating Functions
- Functions Arguments
- Type of Arguments
- Keyworded Variable Length Arguments
- Using Built in Functions
- Global Keywords
- Passing List to Functions
Anonymous Functions / Lambda
Filter / Map Reduce
Decorators
Modules
OOPs Programming
- Modules
- Special variable _name_
- Class & Object
- _init_ method
- Constructor, Self and Comparing Objects
- Inheritance
- Polymorphism:
- Method Overloading
- Method Overriding
Exception Handling
File handling

Adv. Python & Python Analytics

MySQL
- Installation
- CRUD Operations (create, read, insert, update, delete)
Database Integration with Python
- Connection to Database from Python
- Writing functions to handle database operations
- CRUD Operations in python
Web Scrapping
- Beautiful SOUP library
- Parsing HTML files
- Getting Data from Any Website
Python for Data Science -
NumPy & Pandas
- Installing & Understanding Analytics Packages
- NumPy Array Operations
- Pandas Data Frame Operations
- Data Acquisition (Import & Export)
- Indexing
- Selection
- Sorting
- Filtering
- Group By
- Binning
- Concatenation
- Merge
Data Visualization
- Installing Matplotlib
- Charts & Plots
  - Histogram
  - Scatter Plots
  - Box Plots
  - Line Chart
  - Bar Chart
  - Pie Chart etc.

Statistics & Probability

Mean, Median, Mode, Variance, Standard Deviation
Skewness, Kurtosis, Correlation, Covariance
Probability Distributions: Normal, Binomial, Poisson
Sampling, Central Limit Theorem
Hypothesis Testing: t-test, z-test, ANOVA, chi-square
Confidence Intervals, p-values, Type I & II errors
Note – Statistics part will be taken along with ML & AI as and when required

Exploratory Data Analysis (EDA)

Data Preprocessing & EDA Overview
Handling missing values, outliers, duplicates
Encoding (One-hot, Label), Scaling (Standard, MinMax)
Feature engineering, selection techniques
EDA: Univariate, Bivariate, Multivariate analysis
Visuals: Heatmaps, Pairplots, Boxplots, Violin plots

Machine Learning

Supervised Learning
- Linear & Logistic Regression
- Decision Tree, Random Forest, KNN
- SVM with kernel functions
- Naive Bayes, AdaBoost, XGBoost
- Project 1: House Price Prediction
- Project 2: Email Spam Detection
Unsupervised Learning
- K-Means, DBSCAN, Hierarchical Clustering
- Dimensionality Reduction: PCA
- Project 3: Employee Role Segmentation
Model Evaluation & Optimization
- Metrics: Accuracy, Precision, Recall, F1, AUC, R2, RMSE
- Cross Validation, Stratified K-Fold
- Hyperparameter tuning: GridSearchCV, RandomizedSearchCV
- Imbalanced Data Handling: SMOTE, NearMiss

Time Series Forecasting

Time Series Analysis Basics
Trend, Seasonality, Noise, Stationarity
ADF Test, ARIMA, SARIMA
Facebook Prophet, Exponential Smoothing
Project 4: Sales Forecasting

Natural Language Processing (NLP)

NLP Basics
Text preprocessing: Tokenization, Lemmatization, Stopwords
BoW, TF-IDF, Word2Vec
Text Classification, Sentiment Analysis
Project 5: Sentiment Analysis of Product Reviews

Deep Learning

Artificial Neural Networks (ANN)
- Perceptron, Multilayer Perceptron
- Forward & Backward Propagation
- Activation, Loss functions, Optimizers
- Overfitting, Regularization, Batch Normalization
- Project 6: Iris Flower Classification using ANN
Computer Vision (CNNs for Image Processing)
- Convolution, Pooling, Padding
- Transfer Learning (VGG, ResNet)
- R-CNN, Faster R-CNN
- Project 7: Handwritten Digit Recognition
RNNs & LSTMs
- RNN, LSTM, GRU, Bidirectional RNN
- Sequence Modeling, Time Series, Text Generation
- Project 8: Next Word Prediction using RNN & LSTM

(Add on) : Transformers & BERT

Attention Mechanism, Transformer Architecture
Using Pre-trained Models (BERT, RoBERTa)
Embedding generation & fine-tuning
Project 9: Sentiment Analysis using BERT (Transformers)

(Add on) : Generative AI, LLMs (Large Language Models) & OpenAI

GenAI
- What is GenAI? Prompt Engineering
- Use cases: Q&A, Summarization, Image Generation
- Ethical concerns & safety
LLM Apps with OpenAI & LangChain
- Using ollama
- LangChain Pipelines, FAISS, ChromaDB
- Chat with PDF, CSV, Excel
- Project 10: Flow Stack Machine Learning Project

(Add on) : Deployment & MLOps

Web Deployment: Streamlit or Flask
Create AWS Instance
Model Deployment on AWS
Note - this module will be mostly online as trainers aare rare.

Adv. Excel with A.I.

Understand the Excel interface.
Learn basic navigation and data entry.
Excel Interface Overview
- Workbook vs. Worksheet
- Ribbon, Tabs, and Menus
- Cells, Rows, Columns
- Name Box and Formula Bar
Basic Navigation
- Moving around the worksheet
- Selecting cells, rows, and columns
- Using the Go To command
Data Entry and Basic Formatting
- Entering text, numbers, and dates
- Editing cell contents
- Basic formatting (font, size, color, cell alignment)
Saving and Managing Workbooks
- Saving, opening, and closing workbookss
- Save As options (different formats)
Summary Functions: SUM, AVERAGE, MIN, MAX, SUMIF, COUNTIF, AVERAGEIF, SUMIFS, COUNTIFS, AVERAGEIFS
Using AutoSum
Cell Referencing
- Relative vs. Absolute References
- Mixed References
Organize and manage data efficiently
Sorting and Filtering Data
- Sorting by one or multiple columns
- Applying filters to display specific data
- Using Filters using effectively
Using Tables
- Creating and formatting tables
- Table styles and features (Total Row, Header Row)
Data Validation:
Creating drop-down lists
Setting data entry rules
Conditional formatting:
- Conditional formatting using ifs function, greater than, lower than and equal between
- Conditional formatting using colours
- Conditional formatting using colour scales, data bars & Icon sets
Use advanced formulas and functions
Logical Functions
- IF, AND, OR, NOT
** Loopkup Functions **
- VLOOKUP, VLOOKUP WITH MATCH FUNCTION, VLOOKUP WITH COLUMN & ROW FUNCTION, VLOOKUP WITH SEQUENCE FUNCTION, VLOOKUP WITH CURLY PARANTHESIS FUNCTION, VLOOKUP WITH SUMIF, SUMIFS, COUNTIF & COUNTIFS FUNCTIONS, VLOOKUP WITH MATCH FUNCTION, INDEX & MATCH, XLOOKUP, XLOOKUP WITH CONCATENATE FUNCTIONS
Text Functions
- CONCATENATE, LEFT, RIGHT, MID, LEN
Date and Time Functions

TODAY, NOW, DATE, TIME, YEAR, MONTH, DAY, EOMONTH

** Office 365 function **
- Transpose
- Textjoin
- Sequence
- Filter
- Xlookup
- Vstack
- Hstack
- Aggregate and many more
** Pivoting - Analyze data using built-in Excel tools**
Introduction to PivotTables

What is a PivotTable?
Benefits and uses of PivotTables

Creating a PivotTable

Preparing your data
Inserting a PivotTable from various data sources

Understanding PivotTable Components

Rows, Columns, Values, and Filters areas
Field List and Field Settings

Basic PivotTable Operations

Adding and arranging fields
Sorting and filtering data within PivotTables
Grouping data (by dates, numbers, etc.)

Customizing PivotTables

Changing the layout and style
Using different PivotTable layouts (Compact, Outline, Tabular)
Formatting numbers and cells

Calculations in PivotTables

Adding calculated fields and items
Using summary functions (SUM, COUNT, AVERAGE, etc.)
Showing values as percentages, differences, rankings

PivotTable Options and Settings

Understanding PivotTable options
Refreshing data in PivotTables
Changing data source

PivotCharts

Creating PivotCharts from PivotTables
Customizing and formatting PivotCharts
Understanding the relationship between PivotTables and PivotCharts

Advanced PivotTable Techniques

Creating dynamic PivotTables with named ranges
Using Power Pivot to manage large data sets
Connecting PivotTables to external data sources

** Create and customize advanced charts and graphs **
Creating Charts

Column, Bar, Line, Pie, and Combo charts
Customizing chart elements (titles, axes, legends)

Using Sparklines

Creating line, column, and win/loss sparklines

Advanced Chart Techniques

Dynamic charts with named ranges
Using secondary axes

** Automate tasks using Macros and VBA **
Introduction to Macros

Recording and running macros
Macro security settings

Basics of VBA (Recording):

Starting with the recording Macros
Recording of Macros
Saving and running of Macros

Creating Custom Functions

Writing and using custom functions in VBA

** Collaborative and security features **
Sharing and Collaboration

Sharing workbooks
Co-authoring and tracking changes
Comments and notes

Protecting Data

Worksheet and workbook protection
Setting permissions

Excel Online and Mobile

Using Excel in Office 365

** Power Query **
Introduction to Power Query

What is Power Query?
Importance and benefits of using Power Query

Getting Started with Power Query

Installing and accessing Power Query
Power Query interface and key components

Connecting to Data Sources

Types of data sources (Excel, databases, web, etc.)
How to connect to different data sources

Data Import and Shaping

Loading data into Power Query
Basic data transformation (filtering, sorting, removing duplicates)

Transforming Data

Using the Query Editor
Applying common transformations (merging, appending, splitting columns)
Pivoting and unpivoting columns
Grouping data

Data Cleaning

Handling missing values
Data type conversion
Text transformations (trim, clean, replace, etc.)

Working with Functions

Using built-in functions
Creating custom functions
Parameters in Power Query

Advanced Data Transformations

Merging queries
Append queries
Using conditional columns
Calculating and adding new columns

** Scenario manager**
** Dashboards (Project) **
- What are dashboards
- Getting data from differnt worksheets
- Display data in various charts on same sheet etc.
** ChattGPT (A.I. in Adv Excel)**
- Note - Since co-pilot licenseis costly, hence teaching similar topics on ChatGPT
- Introduction
  - Introduction to ChatGPT by OpenAI
  - Benefits of using ChatGPT with Excel
- Using ChatGPT Interface for Excel
  - How to use ChatGPT without subscription
  - Copy–paste workflow with Excel
  - Writing effective prompts for Excel tasks
- Data Analysis Using ChatGPT using Natural Language/li>
- Formula & Function Assistance
  - Generate Excel formulas using AI
  - Explain existing formulas
  - Fix formula errors
  - Optimize and simplify formulas
- Data Cleaning with AI
  - Identify missing or incorrect data
  - Remove duplicates
  - Standardize text, dates, and numbers
  - Suggest cleaning formulas and steps
- Tables, PivotTables & Charts
  - Create Excel tables
  - Generate PivotTable structure using prompts

Power BI with A.I. & Power Automate

Introduction to Power BI
- What is Power BI and Why Power BI
- Installing Power BI Desktop
- Exploring the Power BI Workflow
- Adjusting the settings of the Power BI Desktop
- Comparison of Power BI vs Other Reporting Tools
Getting and Transforming Data with Power BI Desktop
- Connecting to different sources
- Connecting to multiple sources
- Different connecting Options(DirectQuery vs Import Data Vs Live Connection)
- Shaping and transforming data with Power Query
- Editing,Merging,Appending queries etc
Advance Importing techniques without coding
Data cleaning & Advance data cleaning
Modeling with Power BI
- Introduction to Modeling
- Building Relational Models(Setup and Manage Relationships)
- Creating table relationships
- Understanding the filter flow
- Cardinality and Cross filtering
DAX
- Understanding Dax Syntax
- Calculated Columns vs Measures
- Common Dax functions and formulae
- Understanding the evaluation context in DAX
Visualising Data with Reports
- Creating Visualisations
- Color & Conditional Formatting
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Tooltips
- Slicers, Timeline Slicers & Sync Slicers
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- Hierarchies
- Constant Lines
- Tables, Matrices & Table Conditional Formatting
- KPI's, Cards & Gauges
- Map Visualisations
- Custom Visuals
- Managing and Arranging
- Drillthrough
- Custom Report Themes
- Grouping and Binning
- Bookmarks & Buttons
Introduction to Power BI Service
- Introduction to the Power BI Service
- Quick Tour of Power BI Service
- Connecting to Data from Power BI service
- Building Blocks of Power BI Service
Sharing and Collaboration Tools in Power BI Service
- Sharing and Collaboration Options Overview
- Publish from Power BI Desktop
- Publish Reports to Web
- Printing and Exporting from Power BI Service
- Sharing Reports & Dashboards
Power BI Refreshing Datasets
- What is refreshing?
- Implementation
A.I. in Power BI
Advance Report techniques
Power Automate (Latest)
- Introduction
- installation
- Types of flows
- Actions
- Copy multiple excel, pdf files from one folder
- Merge PDF files together, Rename etc.
- Extract phones name and price from amazon
Best Practices

Tableau

Installation and Configuration
Introduction to Tableau
- Architecture
- Data Model
- Data Connection with single data sources
- Data Joins
- Live vs extract connection
- Working with multiple data sources
- Data Blending
- Difference between joining and blending data
Tableau Interface
- Measures and dimension
- Shelves
- Show me
- Data Types
- Default Properties
- Marks Card
- Page Shelf
- Saving and sharing your work-overview
- Difference between .twb and .twbx
- Discrete vs Continuous
Date Aggregations and Date parts
- Cross tab
- Totals & Subtotals
Chart Types
- Bar Charts & Stacked Bars
- Pie Chart
- Line Graph with Date
- Line Graph without Date
- Area charts
- Tree Map
- Word Cloud
- Packed Bubble
- Box & Whiskers Plot
- Scatter Plots
- Highlight tables
- Heat maps
Data Visualization
- Geographical fields
- Map options
Run Time Columns
- Calculated Fields
- String Calculation
- Data Calculation
- Logical Calculation
- Numeric Calculation
- Parameters
- Table Calculations
- Level of detail
Data Formatting
- Legends
- Highlights
- Labelling
- Number Formats
- Alignment
Advanced Functionalities
- Sorting
- Groups
- Sets
- Hierarchy
- Reference and Trend Lines
- Edit Axis
- Bins
Filters
- Dimension Filter
- Measure Filter
- Filter - Display Option
- Context Filters
- Relevant Filters
- Sets in filters
- Conditional Filters
Advanced Charts
- Pareto
- Waterfall
- Funnel
- Waffel Chart
Predictive Analysis
- Forecasting
- Clusters
Dashboards
- Dashboard Objects
- Dashboard Actions
- URL
- Highlight
- Filter
- Advanced Interactivity
- Publish to Web
Story (Adv)
Report designing & exporting
Case Studies / Project
Data Visualization & Analytics
- Data Sources
- from SQL (MySQL)
- from Flat Files (CSV / Excel etc.)
- from Python

Duration

Pkg1 - 10 months - Full Course
Pkg2 - 7 months - (Full Course, Early Completion) - MySQL / Adv Excel / Power BI / Tableau can be taken in parallel one at a time either on same day or on weekends to complete course earlier. Please inquire with institute for more info.
Note - Addon topics (Adv. AI) - duration and fees extra
more info

Fees

Live Online: ~~₹75000~~ - ₹60000 (Limited Period)
Classroom - Get Fees
Upcoming Batches

Tools

Google Reviews

Aishwariya Tari

I have enrolled for data science course. Excellent teaching and supportive environment. Overall a positive experience with the institute. Apurva mam taught us sql and python explaining every concept in clear and concise manner. Aditya sir teaches tableau in wonderful way. He ensures the doubts are cleared and easier to understand. Tejas Sir taught machine learning and AI in an excellent manner by giving apt examples to make us understand the concepts in an easier way.
Pradeep Unde

The emphasis on practical training sets Quick Expert Infotech apart, providing a 100% practical training environment. The trainers are not only skilled but also incredibly cooperative, ensuring that every trainee gets the hands-on experience necessary for success in the field. Trainee Tejas sir's guidance has been instrumental in navigating the complexities of data science, making the learning journey both engaging and enriching. I appreciate the commitment of Quick Expert Infotech to delivering quality education, and I am confident that the skills and knowledge I am gaining here will be invaluable in my IT career. I highly recommend Quick Expert Infotech to anyone seeking a comprehensive and practical approach to data science training.
Mayuri Gujar

I have join QuickXpert infotech for Data science.I got trainer Shreya mam . She is very patient which anyone that takes a language needs – and it’s very comfortable to practice, which is also a huge plus. I recommend her highly. She is an excellent teacher! She provides both a great mix of listening, speaking and practical learning activities. Her manner of teaching is so wonderful and refreshing!! Thank you..!
Aishwariya Alure

In this Data science course, I have learnt many things and enjoyed as well. Lessons are well structured. Incorporating real-world examples in classes to help us understand the concepts. I am happy to have you as my mentor for this course. Thank you for this memorable classes.
Prapti Tamboli

I Join QuickXpert Infotech for Data Science Course. And recently I have completed my course, My heartfelt Thank You to Shreya ma'am for guiding us through this whole course. Your teaching technique makes everything easier for us, to understand each concept properly. You are the best mentor anyone could ask for!! Thank You ma'am!!
Sonali Ghadage

Excellent training institute for data science course. I have join quickexpert for data science,Mrs. Shreya Jain ma'am is our trainer to teach us Data Science. She teach us very friendly and easy way by which we can understand the concept and use them. If we have a doubt and in one time our doubt is not clear then mam will clear our doubt several times until our doubt gets cleared also gives other extra knowledge for our betterment. The quality of the subject that we got from her is outstanding..The quality of the subject that we got from her is outstanding & I am recommend everyone to learn data science & Analyst skills only shreya maam
Rahul Salunkhe

I joined QuickXpert and I completed DataScience course here, it was very good experience to learn here, all the queries and doubts are cleared by Mrs. Shreya Jain at the same time and I have done Practice more than many projects, like Titanic, Flight price predictions , HR Attribute etc.
Nikhil Chikhalkar

I completed a comprehensive Data Science course at Quick Expert Institute, and I am thoroughly impressed with my experience. The course content was well-structured, covering all the essential concepts and tools required for data science, such as Python, machine learning, statistics, and data visualization.What sets Quick Expert Institute apart is their dedicated placement support.
Aniket Magar

I'm pursuing data science course from Quick expert infotech under trainee Tejas sir. This is one of the finest training centre for the IT sector. The faculty and management is always ready to support.100% practical training environment with skilled cooperative trainers.
Pragati Namde

I Join QuickXpert Infotech for Data Science Course. And recently I have completed my course, My heartfelt Thank You to Tejas Tambe sir for guiding us through this whole course. Your teaching technique makes everything easier for us, to understand each concept properly. You are the best mentor anyone could ask for!! Thank You sir !I