From Beginner to PRO

About Our Data Science & Adv. A.I. Certification Course -

Learn from basics of Python to advanced A.I. and Analytics modules with projects to become a Data Science Professional in short duration.
100% Placement Support -
End-to-end placement support with early placement initiation during training (eligibility-based), continuous expert guidance and a simple placement process powered by our dedicated placement team and strong hiring network of 1000+ companies to help you get the best job opportunities and placement faster. Check company list below.
Job Opportunities -
Data Science Engineer, Machine Learning Engineer, Data Analyst etc.
Pre-requisites -
None. Any one from IT & non IT can learn.
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Related Course - Adv. Data Science Training Program (JG)

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Syllabus

Data Science & Anaytics Course

SQL

Oracle Installation
Basics
- Oracle Basics
- Database models
- ER Model Overview
- Data types
- Understanding Test Database
- Basics Queries
- Arithmetic and String functions
- DML Operations - Insert, Update and Delete
- FETCH command - Top N, Top % Rows
DDL
- Tables - Create, Alter, Drop
- Indexes - Types, Create, Drop
- Constraints - Not Null, Primary Key, Unique Key, Foreign Key
- Sequence
- Synonyms
Foreign Keys
DML operations - Insert, Update & Delete
- Insert, Update, Delete & Truncate
- Common Operations
- Creating Tables using queries
- Bulk Data Inserts using Queries
Writing Queries
Filters
- DISTINCT
- BETWEEN
- IN
- LIKE
- NOT
- NULL
- AND, OR, NOT etc
- Using Complex Data Filtering Techniques
Sorting
- Ascending Order
- Descending Order
- Complex Sorting
Functions
- String Functions - lower case, uppercase, substring, instring, trim etc.
- Number Functions
- Date Manipulation
- Null Value Functions - NVL, NVL2, NULLIF, COALESCE
- Conditional Expressions - Case and Decode
Groups
- Basics
- Grouping functions - AVG, MIN, MAX, COUNT, DISTINCT COUNT etc
- Grouping Filters - HAVING
- Rollups & Cubes
Joins
- Cartesian Product
- Equi and Non-Equi Joins
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Self Join
- Complex Queries - Joins & Groups Integration
SET Operators
- Union
- Union All
- Intersect
- Minus
Subquery
- Single Valued Row Sub Query
- Multi Valued Sub Query
Views
Inline Views
ROWID & ROWNUM
TOP N Analysis using Inline Views
Analytic (Window) Functions
- Top N Analysis
- Over() with summary functions
- Partition By Queries
- ROW_NUMBER()
- RANK()
- DENSE_RANK()
Transactions ( T SQL)
- Commit
- Rollback
- Savepoint
DCL - Grant & Revoke
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.

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

Git & GitHub

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)

AI Tools & Prompts

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

Duration

7 Months
Eary completion option (4-5 Months, chosen by many)
• Learn 1 course on weekdays + 1 (less-interconnected) course on weekends for faster course completion and job readiness. more info
Addon topics duration and fees extra

Fees

Online -
₹36000 (3 installments)
₹34000 (2 installments)
₹32000 (1 time)
Classroom -
₹XX000 get fees
Customized Training Available • Fees inclusive of GST • Limited seats per batch • Book Your Free Demo Today • International Students, please convert fees in your currency

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