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.
Engage in learning through our on demand videos released every week plus live doubt-clearing sessions for flexible learning.
Utilize a dedicated Learning Management System for easy access to all course materials, including curated notes, workbooks, and assignments.
Reinforce every theoretical concept taught through practical, real-world coding assignment, solved problems that challenge your understanding.
Develop valuable skills by working on impactful “Data Tales” – solving real-world industry problems and presenting your insights.
Receive dedicated support from experienced mentors through one-on-one consultations, chat, and email interactions.
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
Why python for Data Science, Environment setup, Data types, Variables, methods in Python Lists, List methods, List comprehension, Dictionaries, Dict Methods, Strings – Escape Sequenece, methods,formatted strings, Flow of control – if, if else, for, while loops etc., Exception handling, Lambda, Map Function etc.
Why python for Data Science, Environment setup, Data types, Variables, methods in Python Lists, List methods, List comprehension, Dictionaries, Dict Methods, Strings – Escape Sequenece, methods,formatted strings, Flow of control – if, if else, for, while loops etc., Exception handling, Lambda, Map Function etc.
Introduction to probability,
Exclusivity, Dependency of events
Conditional, Marginal, Joint probability
Additive and Multiplicative rule
Card abadonment problem for food aggregator
Bayes theorem with brand trust.
Common Data Terminology
Types of data – Numerical, Categorical
Central Tendencies – Mean, Median, Mode
Measures of Dispersion – Variance, Spread, Standard deviation
Quartiles and Outliers Detection – boxplot
Probability Mass function, Density Function
Distributions – Normal, Poisson, Binomial – Properties and problems
Concept of Z-score, problems
Sampling distribution of sample means and Central Limit theorem
Verifying the CLT using python code
Confidence Intervals – Detailed explanation
Confidence intervals – proportions case
Hypothesis Testing – Defining P value
Z Test and variants
The need for T Distribution, T Test
Chi square test for goodness of fit
Chi Square test for checking dependency
Why python for Data Science, Environment setup, Data types, Variables, methods in Python Lists, List methods, List comprehension, Dictionaries, Dict Methods, Strings – Escape Sequenece, methods,formatted strings, Flow of control – if, if else, for, while loops etc., Exception handling, Lambda, Map Function etc.
Why python for Data Science, Environment setup, Data types, Variables, methods in Python Lists, List methods, List comprehension, Dictionaries, Dict Methods, Strings – Escape Sequenece, methods,formatted strings, Flow of control – if, if else, for, while loops etc., Exception handling, Lambda, Map Function etc.
Missing values – Sources, Standard, Non standard,
Unexpected NA, treating,
imputing missing values – fixed value, forward fill, backward fill, mean and median imputation
Impute with transformer mixin
Conversion of data types, memory implications
Outliers – Univariate, Multivariate.
Detection using Z score, IQR, scatter plot
Detection using PCA, Treating outliers – capping
Categorical Encoding – dummies and One hot encoding
Scaling – Standard and min max , custom minmax scaling
Scaling numerical variables
Splitting, randomising data for modelling
Cross validation, Grid search, Parameter tuning
The evolution of Artificial Intelligence
Artificial Narrow Intelligence, General Intelligence
Popular problems solved by AI – Regression, Classification, Clustering
Intro to Statistical Modelling, Machine Learning
Intro to Deep Learning, NLP , CV
Concept of Covariance, Correlation
Simple Linear Regression – OLS Method.
Residuals, Assumptions for OLS
Deriving betas for OLS
Evaluation –R Squared, Adjusted R Squared,
Hypothesis testing of coeffs, level of significance
Influential Observations, Transforming nonlinear data
ML way of Solving regression – gradient descent.
Gradient calculation, weight update
Identifying multi collinearity, VIF
Regularisation – Ridge, Lasso
Introducing Classification, popular use cases
Defining Sigmoid function, properties
Formulating logistic model, odds ratio, logit
Cross entropy, Maximum Likelihood estimation
Metrics – Residual Deviance, Misclassification Rate
Metrics – Accuracy, precision, recall, F1 Score
Which metric is appropriate in what situation
ROC, AUC – area under curve
Identifying bias, variance, solutions
Correlations and heatmap,
Linearity, homoscedasticity assumptions verification
Outlier Analysiss and high leverage points
Simple Linear Regression – from scratch, statsmodels, Sklearn
Multiple LR – from scratch, stats models, Sklearn
Multi collinearity, VIF with python code
Linear Regression – indepth – end to end on complex data
Regularisation using ridge and lasso, effect of lamda on regularisation – coefficients
Metrics for regression with python, Visualising regression model
Ridge and Lasso Regression
SGD regressor with sklearn
Employee Attrition as a problem and business usecase,
Data Understanding and Diagnostics,
EDA – Univariate and Bivariate Analysis
Chisquare test to identify dependent variables, Chisquare in case of numeric,
Model Training, evaluation
Precision Recall curve and cutoff
Model Metrics for the usecase – finalise and tune
ROC curve for the model
Introducing trees, Advantages, when to use
TDIDT, measures of goodness
Concept of entropy, information gain
Information content, gains ratio
Chi Square as a purity measure
Pruning, Modelling tips, Termination criteria
CART, C 5.0
Regression trees
Usecase of customer propensity with business outcomes,
Data Understanding, cleaning, missing values & outliers detection
EDA – Univariate , Bivariate Analysis
Getting data model ready
Model Evaluation using classification report, metrics for this model
Fine Tuning the decision tree – depth of tree, min leaves, min samples
Feature importance of Decision Tree, Printing and visualising the Decision Tree
Discriminative versus Generative Classifiers
The concept of Margin, maximising margin – Mathematical Modelling
Defining the hyper plane
Soft margin classifier for Noisy Data
Non Linear data and Data Transformations
Kernel Trick to accelerate non linear transformations
Usecase of customer propensity with business outcomes,
Data Understanding, cleaning, missing values & outliers detection
EDA – Univariate , Bivariate Analysis
Getting data model ready
Model Evaluation using classification report, metrics for this model
Fine Tuning the SVM – Kernel trick, different kernels – when to use
GridSearch on C parameter and fine tuning it
Instance based learning
KNN – Intuition, working way
Distances – Euclidean, Manhattan
Distance – Categorical, Text data
Determining a good K value, right fit
Way to make KNN better
Bagging, Random Forests
Boosting – AdaBoost, XGBoost
Stacking
Working definitions, Methods, Applications
Hard, Soft, Flat clustering
Major approaches – partitioning, hierarchical, Density based
K Means clustering
Hierarchical Clustering, Dendrogram
History of neural networks
Inspiration from Biological neurons, Brain
Artificial Neuron, Perceptron Model
Multi Layer Perceptron model,
Activation functions – sigmoid, tanh, Relu
Stacking layers – need, depth
Error calculation, gradient descent
Stochastic GD, Mini Batch GD
NN Training – issues, intricacies
Fundamental differences between ML , DL
Introducing CNN with Computer vision
Concepts – convolution, stacking, max pooling, Padding
Identity, Edge detection, Operators, Sharpening
Architecture Engineering for DL
LeNet, Alexnet , VGG 16, RESNET, Inception
Auto Encoders – Why, applications
Drop out
Sequence Models, the need
Recurrent Neural Networks – multiple structures
Backprop through Time for RNNs
Language Modelling with RNN
This curriculum equips you with the necessary skills and knowledge to become a successful data scientist.
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.
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
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.
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.
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!
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
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.
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.
 | 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 |
Book a free career counselling session with Prudvi
Download Detailed Curriculum & Success Stories
Access a 1-Day Program Preview for free
Attend a Free Weekend Data Science Bootcamp
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.
