End to End Data Science On Demand

Land Your Dream Job

in 6 Months

This 6-Month Program unlocks unlimited potential, transforming you from a beginner to a Data Scientist. Gain comprehensive training and develop real-world skills that will land you a high-paying job in the field.

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Highring Partners

The future is data-driven

Salaries in Data Science and AI

*source: Data analytics salary study 2024

Why choose

a career in data science

Exciting work

High demand

Great earning potential

At a glance

Program Highlights

01

Interactive learning:

Engage in learning through our on demand videos released every week plus live doubt-clearing sessions for flexible learning.

02

Streamlined learning experience

Utilize a dedicated Learning Management System for easy access to all course materials, including curated notes, workbooks, and assignments.

03

Hands-on application

Reinforce every theoretical concept taught through practical, real-world coding assignment, solved problems that challenge your understanding.

04

Industry-relevant projects

Develop valuable skills by working on impactful “Data Tales” – solving real-world industry problems and presenting your insights.

05

Personalized guidance

Receive dedicated support from experienced mentors through one-on-one consultations, chat, and email interactions.

Whether you want to...

Launch a successful data science career

Advance your career

Enhance your skillset

... this is the ideal program for you!

Tools & technologies

Skills covered

Hands-on learning

Projects you will work on

Dynamic Pricing model for OnDemand Mobility

Estimating the right value for BnB properties

Segmentation of Credit Card Customers

Chatbot for Customer Service using RAG pipelines

Aspect based Sentiment Analysis on Reviews

Get a sneak peak to our ENTIRE program FREE for 2 days

Your learning journey

Ask, learn, repeat

Round the clock, chat support

Get your questions answered promptly and personally. Our mentor never misses a query on Slack, ensuring you receive the guidance you need, when you need it — because your learning journey deserves undivided attention.
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End-to-end data science course - on demand

Detailed curriculum

This curriculum covers a wide range of topics in data science, from foundational statistics to deep learning and natural language processing. Here’s a concise breakdown

1. Data Types in Python
  • Casting Data types in Python
  • List Indexing and Slicing
  • List methods – append
  • List methods – remove elements
  • Keys & values in dicts
  • Add & remove in dicts
  • String escape Sequences
  • String Operations
  • String indexing and slicing
  • String methods
  • String methods cont
  • String formatting
  • Loops in Python- for loop
  • Probability Mass Function Defining
  • Probability Density Function
  • Concept of Cumulative Mass Function
  • Binomial Distribution Explained with real example
  • Normal Distribution Properties, Skewness
  • Normal Distribution – area computation
  • Poissons Distribution
  • Python Lambda function
1. Understanding Probability
  • Practical use case of probability
  • Calculate probability of event
  • Single, multiple events and exclusivity
  • Additive Rule of Probability
  • Additive Rule with joint probability
  • Conditional Probability
  • Defining Dependent Events
  • Defining Multiplicative rule and joint Probability
  • Solving problems with Multiplicative rule
  • Introducing the Tree Diagram with types of Probability
  • Type of Probability with Cart Abandonment
  • Bayes Theorem Explained with brand trust example
  • Bayes Theorem for covid virus diagnosis
  • Bayes Theorem continued – Solving the issue with diagnostic report
  • Dependent versus Independent Variables
  • Types of Data – Numeric versus Categorical
  • Calculation of IQR and boxplots
  • Using IQR to make sense of data
  • Problems on Std Dev
  • Probability Mass Function Defining
  • Probability Density Function
  • Concept of Cumulative Mass Function
  • Binomial Distribution Explained with real example
  • Normal Distribution Properties, Skewness
  • Normal Distribution – area computation
  • Poissons Distribution
1. Introducing NumPy array
  • Creating Numpy array
  • Creating 2d numpy array
  • Slicing numpy array
  • Negative index in numpy array
  • Traversing array in reverse direction
  • Reshape numpy array
  • Transposing numpy array
  • Rounding numbers in numpy
  • Cumulative of Np array
  • Common elements in NumPy arrays
  • The concept of seed in random
  • Generating random numbers
1. Introducing Inferential Statistics
  • Exercise on Central Limit Theorem
  • Verifying CLT using python
  • Confidence Intervals – Explained
  • Confidence Intervals with a real Problem
  • Solving Confidence Intervals with Python Scipy
  • Confidence Intervals using Proportions
  • Shortcuts for Confidence Interval Calculation
  • Defining the Hypothesis Testing on example
  • P value In hypothesis Testing
  • Verifying Hypothesis Testing
  • Errors in Hypothesis Testing
  • Errors in Hypothesis Testing continued
  • Problems on Hypothesis testing
  • Problems Continued on Hypothesis testing
  • T distribution for confidence Intervals
  • T Distribution for Hypothesis Testing
  • Chisquare Goodness of fit
  • Chisquare Dependency of Variable
1. Introduction to Pandas
  • Loading a data frame from csv
  • Data Frame attributes and properties
  • Descriptive Stats for Data frame
  • Finding Missing Values in dataset
  • DF Operations – Accessing multiple 
columns in a list
  • DF operations – Indexing, Resetting index
  • DF operations – Slicing with Iloc
  • DF Operations – Slicing with Loc
  • DF operations – Filtering based on condition
  • DF operations – Value Counts using DF
  • DF operations – Sort Data Frame
  • Data Munging – Binning
  • Data Munging – Concat Dataframes
  • Data Munging – Merging Data Frames
  • Data Munging – Crosstab.mp4
  • The concept of seed in random
  • Generating random numbers
1. Our first Basic Line Plot
  • Line Plot with X, y, title
  • Line Plot with Line styles, markers
  • Line Plot adding Multiple Lines
  • Line Plot – Changing Xlim Ylim
  • Line Plot – Annotate
  • Line Plot – Real data
  • Line Plot – Multiple Lines
  • Scatter Plot – Point color
  • Scatter plot – Colors using c
  • Scatter Plot – Annotating a plot
  • Scatter Plot – Summarised
  • Scatter plot – real data
  • Scatter Plot – Creating multiple clusters
  • Scatter Plot – Clustering
  • Histogram – Step curve
  • Histogram- Stacked data
  • Histogram – Real data
  • Histogram – Custom bin widths
  • Histogram – Real data
  • Bar Plot – Real data
  • Bar Plot – Errors, Stacked
  • Bar Plot – Seaborn, Errors
  • Box Plot- Customising Box plot
  • Additional Plots – Strip plot
  • Additional Plots – LM plot
  • Pairplot
  • Pie plot
1. Data Cleaning tutorial overview
  • Checking for red flags in dataset
  • Irrelevant rows in data – Identify and remove
  • Duplicated rows in Data – identification, solution
  • Non Standard values
  • Structural Errors – Correction
  • Non Standard Missing Values Identification Numeric Variables
  • Non Standard Missing Values Identification – Unexpected Missing Values
  • Summarising missing values
  • Impute with fixed value
  • Impute with forward and backward fill
  • Impute with Transformer mixin function
  • Outlier detection approaches
  • Inter Quartile Range for Outlier detection
  • Outlier Detection using Z score method
  • Outlier Detection using PCA and visual inspection
  • Categorical Encoding – Sklearn onehot encoder
  • Label Encoding – sklearn
1. Introducing regression
  • Issues with Covariance
  • Deriving the betas – SLR
  • RMSE
  • Coefficient Analysis
  • R Squared
  • Evaluation – Adjusted R Squared
  • Tuning learning rate in Gradient Descent
1. Correlations
  • Correlation on real data – spearman & pearson method
  • Correlations with heatmap
  • Verify Regression assumptions heterscedasticity
  • Differentiating Linear and Non Linear Relationship
  • Removing outliers for regression
  • Simple Linear Regression – Interpreting SM OLS
  • Multiple Linear Regression – Metrics
  • Correlations
  • Processing data
  • Model fitting and metrics
  • Achieving Regularisation with Ridge Regression
  • Achieving Regularisation with Ridge Regression
  • Printing metrics of regression models
1. Logistic Regression
  • Log Reg Defined
  • Logistic Mathematical Modelling
  • Confusion Matrix
  • Precision Recall
  • Precision Versus Recall with usecase
  • Roc AUC as a metric
1. Introducing the attrition problem
  • Data understanding, Diagnostics
  • Datatype conversion
  • EDA Understanding Target Variable
  • EDA Bivariate Analysis on DataSet
  • EDA Additional Insights from Data.
  • Chisquaare test between categorical and target
  • ROC Area under curve for evaluation
  • Precision Recall Curve
1. Defining Decision Trees
  • Step wise building of Decision Tree
  • Information Gain as Metric
  • Gains Ratio as metric
  • Gini Index as purity metric
  • Chisquare purity metric, when to use dtree
1. Introducing the problem and Dataset
  • Data Understanding
  • Data Cleaning
  • Data Cleaning – Missing Values
  • EDA Count Plot
  • Eda Bivariate Analysis 1
  • EDA – Bivariate Analysis 2.mp4
  • Maximising the recall or precision
  • Fine tuning Decision Tree
1. Introducing SVM
1. Understanding Probability
  • Practical use case of probability
  • Calculate probability of event
  • Single, multiple events and exclusivity
  • Additive Rule of Probability
  • Additive Rule with joint probability
  • Conditional Probability
  • Defining Dependent Events
  • Defining Multiplicative rule and joint Probability
  • Solving problems with Multiplicative rule
  • Introducing the Tree Diagram with types of Probability
  • Type of Probability with Cart Abandonment
  • Bayes Theorem Explained with brand trust example
  • Bayes Theorem for covid virus diagnosis
  • Bayes Theorem continued – Solving the issue with diagnostic report
  • Dependent versus Independent Variables
  • Types of Data – Numeric versus Categorical
  • Calculation of IQR and boxplots
  • Using IQR to make sense of data
  • Problems on Std Dev
  • Probability Mass Function Defining
  • Probability Density Function
  • Concept of Cumulative Mass Function
  • Binomial Distribution Explained with real example
  • Normal Distribution Properties, Skewness
  • Normal Distribution – area computation
  • Poissons Distribution
1. Introducing KNN
1. Introduction to neural networks
  • Activation Functions – In Detailed
  • Refined Versions of Relu, Bias
  •  
1. Build your first AI app - chatbot or Code an online Saas in Minutes
1. Prompt Engineering &  Text Generation
1. Building our own LLM : Using the transformers
1. How to classify anything or everything using LLMs
  • EDA Understanding Target Variable
  • EDA Bivariate Analysis on DataSet
  • EDA Additional Insights from Data.
  • Chisquaare test between categorical and target
  • ROC Area under curve for evaluation
  • Precision Recall Curve
1. Dense Retrieval, Reranking and Evaluation of Retrieval
1. Types of Agents - Control, Autonomy,
1. Exploring ViT architecture in detailed, diffusion models

This curriculum equips you with the necessary skills and knowledge to become a successful data scientist.

Get recognised

Certificate of completion

Meet the Instructor

Prudhvi P

Gold medalist at NIT Warangal with 6 years of experience

Founder & Chief Mentor at Supervised Learning.

Success stories

See what our students say

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Mohit Ranawat

I have successfully made a career transition into DL. Thanks to lively teaching and practical examples which helped me solve any problems and improved my problem-solving capabilities. The data tales provided were some of the best things I have ever seen.

Mohit Ranawat
Sr. Technical Analyst, Cognest.ai

Harish J

As a student of the NLP 100 Hours program, I can say, we got the best instructor. Great case studies, exhaustive hands-on, Good interactivity sessions, and a diverse batch for good networking are the best part of the course.

This is the best course for NLP

Harish J
Data Scientist , Tech Mahindra

Shravya CH

Today was my first day at IIT Hyderabad and making it here as the youngest learner was only possible because of Prudhvi’s mentorship.  His way of simplifying concepts and analogies made it easier to recall. Happy to have undertaken this course and this course helped me make my IT dream come true.

Shravya CH
Data Scientist, Accenture

Testimonials

Here’s what they say about our instructor

Meenakshi Mishra

Data Science Leader

I can recommend Prudhvi as a enthusiastic Data Scientist, who is not afraid to dig in most complex algorithm logic, finding a way how to implement new techniques or fix some cumbersome code issue.

Vasu Madasu

Sr Manager, Data Science

Prudhvi’s indepth knowledge,  is evident in teaching as he seamlessly synchronizes with students at all stages. While his structured  live classes  sink in concepts, his practical examples , hands-on practice helps to retain them!

Veena Vemula

Senior Data and Analytics Manager

The training provided by Prudhvi is a uniquely comprehensive curriculum, one of the best I have come across. Perfectly balances fundamental of Math and Statistics, gradually progressing into complex ML , Gen AI, making the transition seamless

Meenakshi Mishra

Data Science Leader

I recommend Prudhvi for his approach to deliver the content is amazing & updated with market requirements. The assignments given are very well drafted that will test your knowledge. I thoroughly enjoyed his classes.

Real users. Real feedback.

Descriptive statistics were explained clearly

Excellent discussion on descriptive statistics

In-depth coverage of the Probability and real time examples

I liked the clear explanation about data statistics

In detail explanation of probability which was never seen.

I like the way of teaching in detailed and making sure that everyone understands

Relevance of the teachings in real life based scenarios and It was engaging too.

Its great to see required basics of Maths is getting covered here

Bootcamp experiences

Real users. Real feedback.

Excellent discussion on descriptive statistics

Descriptive statistics were explained clearly

In-depth coverage of the Probability and real time examples

I liked the clear explanation about data statistics

In detail explanation of probability which was never seen.

Its great to see required basics of Maths is getting covered here

Relevance of the teachings in real life based scenarios and It was engaging too.

I like the way of teaching in detailed and making sure that everyone understands

Supervised learning

About us

SupervisedLearning.com has successfully trained 500+ students across 24 batches. Founded by Prudhvi Potuganti, a gold medalist with 8 years of Data Science experience and 60+ research citations, the platform is your gateway to excellence.

Trained 10,000+ students & placed in 200+ companies worldwide

Book a FREE! career counselling session with Prudvi

WHY CHOOSE US

How we compare

Others
Live online classes
Yes
No
Curriculum
Detailed, vetted by industry
Vague
Course fee
High Value
Expensive
Mock interviews
Yes
No
Placement opportunities
Yes
No
Chat support from mentor
Yes
No
Real-time projects
Yes
No

Our achievements

Awards & Recognition

your Learning investment

Program fee

Basic

Course Fee INR 9000

Secure your spot now!

Take the first step

Still in doubt?

Get expert guidance for your career path

Book a free career counselling session with Prudvi

Explore the course and alumni success

Download Detailed Curriculum & Success Stories

Sneak peak into our hands-on learning

Access a 1-Day Program Preview for free

Join an immersive weekend session

Attend a Free Weekend Data Science Bootcamp

Frequently asked questions

Recent graduates who are from either Technology, Engineering ,Mathematics and Statistics are very much welcome

Our the last 5 years, we have extensively focused on qualitative delivery and keeping curriculum on par with industry expectations with assured placements

This course is designed keeping working professionals in mind and works well for them

Around 6-7 months

Excellent Teaching methodolgy, Detailed curriculum, Attention to details in learning curve of students, Placement Support, Project oriented learning – all makes us unique and successful

While computer graduates can excel at Data Science careers well, but it’s not just meant for them. AI / DS careers are for everyone who can do good in math, analysis, code

Careers in AI / DS are one of the fastest growing options. If you are resuming after a career break, this is the perfect opportunity

A Data Engineer in an organisation looks after plumbing side of data. Managing where data is stored, how it’s stored, how it’s moved across organisation. Depending on Scale of organisation, the technologies do change. Data Scientists build models, extract insights and make data usable

Yes. High School math. We teach it as a part of the course

Knowing helps. But we teach it as a part of the course

Applied Stats, Machine Learning, Deep Learning and Generative AI with Data Analysis, Visualisation skills.

Yes. AI is included. ML, Deep Learning and Generative AI are the components in AI

Yes. AI is included. ML, Deep Learning and Generative AI are the components in AI

 Which market isn’t crowded ? There is pie for everyone. If the field interests you, work harder for a bigger pie

Data Analyst deals with writing queries and extracting data and building Dashboards. Data Scientists work on building models with Data

Yes. It does. HBR in 2022 said that till 2029, DS / AI careers are going to be the fastest growing

You can aim for Data Analst, Data Scientist, ML Engineer, AI Engineer , Decision Scientist etc kind roles.

A lot. In India alone, every month close to 1000 companies are hiring for 5000 roles.

Yes. We show you opportunities where you can apply for Data Sciene roles

Yes. We review our learners resumes atleast 2-3 times

Yes. We conduct mock interviews.

Ask us on the slack channel or join on q/a sessions happening every week

During those sessions, a live mentor will join where you can ask questions and resolve queries.

Kickstart your data science journey.

Grab your spot now for FREE!