Data Science and AI Training

Data Science Online Course with Machine Learning and AI

Learn SQL, Python, statistics, machine learning, deep learning and AWS deployment in one data science online course. Choose live online, classroom or self paced learning. You receive a certificate of completion.

★ 4.3 out of 5 rating 15k+ learners trained Certificate on completion

Live Interactive Classes
Job Assistance
Industry Expert Trainers
Recorded Classes on LMS
Hands On Practicals
Online and Classroom Batches

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Course Duration

[Confirm duration]

Learning Mode

Live Online, Classroom and Self Paced

Projects

Real Time Project

Certification

Certificate of Completion

Job Support

Job Assistance

Batch Timing

[Confirm batch timings]

Experience Level

Beginner Friendly

Fees Start At

₹5,000

Who Should Take This Data Science Course for Beginners?

This course is for:

Beginners

Start with SQL and Python basics before you move into statistics and machine learning.

Working Professionals

Move into data roles or add data skills with live classes and recordings on your LMS.

Software Developers

Add data analysis, machine learning and deployment skills to your programming.

Analysts

Go from reports and dashboards to prediction models.

Not sure which course is right for you?

Take our 2 minute course finder to choose between Data Science, Data Analyst, Python and Generative AI.

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What You Will Learn in the Python Data Science Course

SQL Python NumPy and Pandas Visualization Big Data Statistics and Machine Learning Deep Learning AWS Deployment

By the end of the course, you'll be able to:

Write SQL queries and program in Python with NumPy and Pandas
Clean and analyse data, including missing data, group by, merge and join
Visualise data with Matplotlib, Seaborn, Plotly and Tableau
Apply descriptive and inferential statistics, probability and the central limit theorem
Build and evaluate machine learning models for regression, classification, clustering and forecasting
Work with big data using Hadoop and Spark, including Spark MLlib
Explain neural networks and build deep learning models such as CNN and RNN
Deploy a model on AWS with S3, EC2 and Amazon SageMaker, and use R and Git in your workflow

Data science tools and technologies

SQL

Python

Pandas

Scikit Learn

Hadoop and Spark

R

AWS SageMaker

Tableau

Hands On Practice and Industry Project

Practical 01

Python Exercises

Tools: Python, Anaconda

You'll build:

Python installed with Anaconda
Your first Python program
Functions with and without parameters, and lambda expressions
A class with a member function and an object

View Practical →

Project 02

Hadoop Data Loading

Tools: Hadoop shell and copy commands

You'll build:

Data loaded into a Hadoop cluster
Practice with common Hadoop shell commands
Practice with Hadoop copy commands
Hands on work with Hadoop cluster architecture

View Project →

Project 03

Model Deployment on AWS

Tools: Amazon S3, EC2, SageMaker

You'll build:

Data stored in S3
A deep learning instance on EC2
A model trained and tuned with SageMaker
A model built and deployed with SageMaker

View Project →

Each project gives you something specific to explain in an interview.

Data Science Career Roles After This Course

Course

Skill Building

Hands On Practicals

Real Time Project

CV Help

Interview Preparation

Job Assistance

Career roles you can target

Role	Core Skills
Junior Data Scientist	Python, statistics, machine learning
Machine Learning Trainee	Scikit Learn, ensembles, deep learning, SageMaker
Data Analyst	SQL, Pandas, Tableau
Big Data Analyst	Hadoop, Spark, SQL
BI Analyst Trainee	Tableau, SQL, visualization

Job assistance is described below with what it includes. These roles are examples of where the skills are used. This course does not guarantee a job offer.

How Prwatech supports your job search

What job assistance includes, in detail.

01

CV Help

Get help creating a professionally written CV.

02

Interview Preparation

Get guidance for interview preparation and common interview questions.

03

Real Time Project

Work on an industry grade project to see how data teams work.

04

Screening Test

Learners who pass the screening test receive interview calls.

05

Job Assistance

[Describe exactly what else this includes, such as referrals or a job board.]

92% learners placed since 2010

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Why Learn Data Science with Prwatech

15+ Years

Training Experience

15k+

Learners Trained

Industry

Experts Lead Live Q&A

Recorded

Classes on Your LMS

Job

Assistance Included

Online +

Classroom Batches

Data Science Trainers

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[Trainer Name]

[Current role]

[Years of experience]

Specialization: [Specialization]

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[Trainer Name]

[Current role]

[Years of experience]

Specialization: [Specialization]

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Student Reviews

★ 4.3 average from 299+ verified Google reviews

Every review is public on Google.

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Before → After

Before	After
No coding experience	Python programs and SQL queries
No statistics background	Descriptive and inferential statistics
No model building experience	Regression, classification and clustering models
No deployment experience	Model deployment on AWS with SageMaker

Data Science Course Subjects and Syllabus

1. SQL

Databases, DBMS and MySQL
Create, select, update, delete and truncate
SQL data types, primary keys, null and not null values, auto increment
WHERE with AND, OR, NOT, IS NULL and IS NOT NULL
MIN, MAX, COUNT, AVG, SUM and DISTINCT

2. Python Programming

Python setup: Anaconda on Windows, Mac and Linux, and a Python IDE
Basic commands, built in data types, variables, keywords, classes, modules and strings
Operators: arithmetic, relational, logical, identity, bitwise and assignment, precedence and type casting
Tuples, sets and frozen sets, and dictionaries
Program control flow: if statements, for and while loops, nested loops, break, continue and the range function
Hands on: first program, functions, lambda expressions, classes, member functions and objects

3. Python for Data Analysis and Visualization

NumPy: vectors and matrices, array functions, indexing and selection, array operations and universal functions
Pandas: Series and DataFrames, missing data and imputation, group by, merge, join and concatenate, reading and writing CSV, Excel, database and HTML data
Matplotlib: plots, subplots, axes, labels, titles, figure size, dpi, markers and special plot types
Seaborn: distribution, categorical, matrix, grid and regression plots, styles and colors
Pandas built in plots: histogram, area, bar, scatter, box, hex, KDE and density plots, and choropleth maps
Interactive world and US maps with Plotly and Cufflinks

4. Big Data Analytics

What big data is, distributed computing and parallel processing
Why data scientists need big data
Hadoop architecture: cluster architecture and modes, multi node cluster, MapReduce job execution, shell commands, data loading and copy commands, and a data loading project
Spark: introduction, architecture, functional programming, collections, Spark Streaming, Spark SQL and Spark MLlib

5. Statistics and Machine Learning

Statistics and probability: mean, median, mode, variance, standard deviation, random variables, probability density functions, binomial distribution, central limit theorem, sampling distribution and standard error
Machine learning introduction: process flow, supervised, unsupervised and reinforcement learning, Scikit Learn overview
Regression: linear regression, exploratory data analysis, correlation and feature selection, performance evaluation, polynomial, regularized (Ridge, Lasso and Elastic Net) and multiple regression, data pre processing
Decision trees: CART, ID3, C4.5, C5.0, Gini impurity, entropy, information gain, decision tree regression and visualization
Logistic regression: sigmoid function, SGD classifier, cross validation, confusion matrix, precision, recall, F1 score and ROC curve
K nearest neighbors, and support vector machines: margin, support vectors, kernels, grid search and support vector regression
Ensemble methods: bagging, random forest, Extra Trees, AdaBoost, gradient boosting and XGBoost
Unsupervised learning: hierarchical clustering, agglomerative clustering and K Means
Forecasting: error metrics, model based and data driven approaches, exponential smoothing and ARIMA
Dimensionality reduction: benefits, approaches and common techniques

6. R Programming, Git, Data Mining and Data Cleaning

R and RStudio: building blocks, data manipulation, matrices, built in functions and visualization
Git: distributed version control, creating a repository and the basic commands (status, add, branch, checkout, log, pull, push and commit)
Data mining: techniques, architecture and outliers
Data cleaning and data processing in data mining

7. Model Deployment on AWS

Cloud computing and AWS basics
Storing data in Amazon S3
Creating a deep learning instance on EC2
Training, tuning, building and deploying models with Amazon SageMaker

8. Artificial Intelligence, Deep Learning and Tableau

Why deep learning, its strengths and limits
Neural network overview and representation, activation and loss functions, gradient descent
Train, test and validation sets, vanishing and exploding gradients, dropout, regularization, optimization, learning rate, tuning and softmax
CNN: deep convolution models, detection algorithms and face recognition
RNN, LSTM and bidirectional LSTM
Tableau: ETL and data analytics overview, Tableau architecture, and the Tableau products Desktop, Server, Online, Mobile, Public and Reader

Data Science Course Price and Learning Formats

All formats include the same curriculum and projects.

Swipe sideways to compare all three formats →

	Self Paced	Most Popular Live Online	Classroom
Recorded Content	✓	✓	✓
Live Classes	Not included	✓	✓
Trainer Support	Limited	✓	✓
Projects	✓	✓	✓
Career Support	[Specify]	✓	✓
Access	Lifetime	[Duration]	[Duration]
Price	₹5,000 or ₹[X,XXX] per month EMI	₹30,000 or ₹[X,XXX] per month EMI	₹30,500 or ₹[X,XXX] per month EMI

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1

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2

Advisor calls you

3

Discuss background & goals

4

Choose suitable batch

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Complete enrollment

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Start learning

Frequently Asked Questions

What is a data science course and who can join?

A data science course teaches you to collect, clean, analyse and model data to support decisions. This course covers SQL, Python, statistics, machine learning, deep learning, big data, AWS deployment and Tableau. Beginners, working professionals, software developers and analysts can join. The course starts with SQL and Python basics, so you do not need earlier coding experience. [Confirm the minimum qualification, if any.]

What is the data science course duration?

The course runs for [Confirm duration].

What is the data science course fee?

The live online course fee is ₹30,000. The classroom fee is ₹30,500. The self paced course costs ₹5,000. Contact the learning team for EMI details.

What subjects are in the data science course?

SQL, Python, NumPy and Pandas, visualization, big data with Hadoop and Spark, statistics, machine learning, R, Git, data mining, AWS deployment, deep learning and Tableau. See the full syllabus above.

Is machine learning included in this course?

Yes. You build regression, classification, ensemble, clustering and forecasting models, and you learn deep learning with CNN and RNN.

Do I learn Python or R?

Python is the main language. You also learn the basics of R in one module.

Does the course include AI?

It includes neural networks and deep learning, with CNN, RNN and LSTM. It does not cover generative AI. For that, see our Generative AI course.

Do I get a data science certification?

You receive a Prwatech certificate of completion, and it is included in the course. It is a course certificate and not a vendor exam. Preparation for a vendor exam is not part of this course.

Do you provide job assistance?

Learners who pass the screening test receive interview calls. We also help with your CV and with interview preparation. We do not offer a job guarantee and we do not guarantee a job offer.

What if I miss a live class?

Every class is recorded and uploaded to your LMS. Earlier class recordings and presentations are also available to you.

Start Learning Data Science

Get the complete curriculum and speak with a learning advisor about the right learning format for you.

Download Curriculum Book a Free Demo Class

Data Science Online Course with Machine Learning and AI

Who Should Take This Data Science Course for Beginners?

Beginners

Working Professionals

Software Developers

Analysts

What You Will Learn in the Python Data Science Course

Hands On Practice and Industry Project

Python Exercises

Hadoop Data Loading

Model Deployment on AWS

Data Science Career Roles After This Course

How Prwatech supports your job search

CV Help

Interview Preparation

Real Time Project

Screening Test

Job Assistance

Why Learn Data Science with Prwatech

Data Science Trainers

[Trainer Name]

[Trainer Name]

Student Reviews

Data Science Course Subjects and Syllabus

Data Science Course Price and Learning Formats

What happens after you enquire?

Frequently Asked Questions

Start Learning Data Science

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