From Beginner to PRO
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Adv. Data Science & Analytics with Gen AI (Data Scientist) Training Program Overview -
Learn from basics of Python to Adv. Data Science modules like Gen AI with industry leading analytic tools like Power BI, Tableau etc and Database from Experts with Live Projects to beceome a Data Scientist.
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Job Opportunities -
Data Science Engineer, Data Scientist, Machine Learning Engineer, Data Analyst, Data Analytics Engineer etc.
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Pre-requisites -
None. Any one from IT & non IT can learn.
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Get Offers & Details
Syllabus
- 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
- 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
- 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.
- 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
- 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
- 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 Analysis Basics
- Trend, Seasonality, Noise, Stationarity
- ADF Test, ARIMA, SARIMA
- Facebook Prophet, Exponential Smoothing
- Project 4: Sales Forecasting
- NLP Basics
- Text preprocessing: Tokenization, Lemmatization, Stopwords
- BoW, TF-IDF, Word2Vec
- Text Classification, Sentiment Analysis
- Project 5: Sentiment Analysis of Product Reviews
- 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
- Attention Mechanism, Transformer Architecture
- Using Pre-trained Models (BERT, RoBERTa)
- Embedding generation & fine-tuning
- Project 9: Sentiment Analysis using BERT (Transformers)
- 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
- Web Deployment: Streamlit or Flask
- Create AWS Instance
- Model Deployment on AWS
- Note - this module will be mostly online as trainers aare rare.
- 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
- 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
- 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
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- Data Sources
- from SQL (MySQL)
- from Flat Files (CSV / Excel etc.)
- from Python
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Git & GitHub Basics
- What is Git?
- What is GitHub?
- Difference between Git and GitHub
- Local Repository vs Remote Repository
- Initializing a Git project (git init)
- Cloning a repository (git clone)
- Staging changes (git add)
- Commit changes (git commit -m)
- Push code to GitHub (git push)
- Pull latest code (git pull)
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Branching & Collaboration
- What is a branch?
- Create and switch branches
- Merge branches
- Basic understanding of pull requests (PR)
- Handling simple merge conflicts
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GitHub Workflow
- Create repository on GitHub
- Push local project to GitHub
- README.md usage
- Public vs Private repositories
- Basic project version tracking
- Popular AI Tools & AI Assistants
- ChatGPT
- Gemini
- Claude
- Perplexity
- Free vs Paid AI Tools
- Choosing the Right Tool
- Prompt Engineering Fundamentals
- What is Prompt Engineering?
- Components of a Good Prompt
- Basic Prompting
- Role-Based Prompting
- Common Prompt Mistakes
- Improving AI Output Quality
- AI for Technical Tasks
- Weekdays:
- Option 1 - 10 to 11 months - 1.5 to 2 hrs per day
- Option 2 - 6 to 7 months - 2.5 hrs per day
- Weekends:
- 8 to 9 months - 2.5 to 3 hrs per day
- Early Course Completion - Interested students can undertake non related courses on same day or 1 in week day and other on weekends to complete the course early and get ready for the job early. Please contact institute for more info.
- Bootcamp training available
- Batch Types - Weekdays - MTTF or Weekends - SS
- Need Info ? Inquire Now
- Rs. 80000 (installments)
- Rs. 75000 (1 time)
Tools
Google Reviews
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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
Training Samples
Few Live Batch Examples (7 mins)