End to End Data Science Live Class

A flagship program for Working professionals covering essentials of Data Science, AI and mentoring till you become data scientist.

Classes are conducted on Zoom calls with 24*7 unlimited access to recordings for 9 months

2 weeks no questions asked full refund plus no cost EMI available with our partners.

With our slack channel, interact and get help with mentor who teaches or support staff anytime during work hours

During the mentoring phase, we share 25 solved Data Tales which are nothing but beautifully woven stories with code, data, visuals and insights

Our curriculum is continuously monitored, reviewed and updated by Industry expert

Receive a course completion certificate from supervisedlearning.com valued by Industry experts.

Get our slides, tutorials, Codes, Assignments with solutions, Class notes, Use cases with codes for life.

If this batch doesn’t work for you, move to next batch without any hassle free of cost.

Successful students get discounts on advanced courses like NLP 100 hours course

Your learning will be enhanced with our thought provoking assignments with solutions, timely quizzes and problem statements to work on

Module 1: Introduction to data science

  1. What is data science and  How is data science different from BI and Reporting?
  2. What is the difference between AI, Data Science, Machine Learning, Deep Learning
  3. Job Landscape and Preparation Time
  4. Who are data scientists and What skillsets are required?
  5. What is day to day job of Data Scientis and What kind of projects they work on?
  6. End to End Data Science Project Life Cycle
  7. Data Science roles – functions, pay across domains, experience

Module 2: Descriptive and Inferential statistics

  • Probability concepts, Bayes Theorem , PMF, PDF , CMF , CDF 
  •  Mean, Median , Mode, Std Dev , Box Plots 
  • Sampling , Normal, Binomial, Binomial approximation to normal
  • Z score calculation and defining p-value 
  • Sampling distribution of sample means
  •  Hypothesis testing , Confidence Interval ,Type I,II  error
  • T Test , Chi Square test

Module 3: Programming with R

  1. A Primer to R programming
  2. Types of objects in R – lists, matrices, arrays, data.frames etc.
  3. If statement, conditional loops - for, while etc., String manipulations
  4. Sub setting data from matrices and data.frames
  5. Casting and melting data,  Merging datasets

Module 4: Python for Data Science

  1. Understanding the reason of Python’s popularity
  2. Basics of Python: Operations, loops, functions, dictionaries
  3. Numpy – creating arrays, reading, writing, manipulation techniques
  4. Ground-up for Deep-Learning

Module 5: Exploratory Data Analysis with Python

  1. Getting to understand structure of Matplotlib
  2. Configuring grid, ticks.
  3. text, color map, markers, widths with Matplotlib
  4. configuring axes, grid, hist, scatterplots
  5. bar charts , multiple plots, 3D plots
  6. Correlation matrix plotting

Module 6: Data Munging with Python

  1. Introduction to pandas
  2. Data loading with Pandas
  3. Data types with python
  4. Descriptive Statistics with Pandas
  5. Quartile analysis with Pandas
  6. Sort, Merge, join with Pandas
  7. Indexing and Slicing with pandas
  8. Pivot table, Aggregate and cross tab with pandas
  9. Apply function for parallel processing with Python
  10. Cleaning Data with python
  11. Determining correlation
  12. Handling missing values
  13. Plotting with Pandas
  14. Time series with Pandas

Module 7: Introduction to Artificial Intelligence

  1. Dealing Prediction problem
  2. Forecasting for industry
  3. Optimization in logistics
  4. Segmentation in customer analytics
  5. Supervised learning
  6. Unsupervised Learning
  7. Optimization
  8. Types of AI : Statistical Modelling, Machine Learning, Deep Learning, Optimization, Natural Language Processing, Computer vision, Speech Processing, Robotics

Module 8: Statistical Modelling

  1. Linear Regression - Assumptions, Model development, Interpretation , Model validation ,                     Multiple linear regression
  2. Logistic Regression - Logit link function, Maximum likelihood estimation, Model development,         Confusion Matrix, ROC curve
  3. Time series analysis - Forecasting - Simple moving averages, Exponential smoothing , Time series decomposition, ARIMA

Module 9: Machine Learning - Supervised

  • Decision trees and Random Forest
  • Association Rule Mining - Apriori and FP Growth
  •  SVM 
  • Neural Network: Perceptron and Multi-Layer Perceptron
  • Ensemble Techniques
  • Gradient Boosting Machines

Module 10: Machine Learning - Unsupervised

  1. Hierarchical clustering
  2. K-Means clustering
  3. Distance measures
  4. Applications of cluster analysis – Customer Segmentation
  5. Collaborative Filtering, PCA

Module 11: Natural Language Processing

Tokenization, Stemming, Lemmatization

  •  Text Modelling 
  • POS tagging
  • TFIDF and classification
  • Sentiment Analysis

Module 12: Deep Learning And Optimisation

  1. ReLU , Sigmoid, Depth vs Width tradeoffs
  2. Convolutional networks for vision - CNNs
  3. Concepts of filters , Sliding , Pooling, and Padding
  4. Comparison between DL and ML performances over the MNIST dataset
  5. RNNs and LSTM

Module 13: Practical use cases of AI and best practices in AI

  1. Business problem to an analytical problem
  2. Guidelines in model development

Module 14: Analytical Visualisation with Tableau

  1. Why is it important for Data-Analyst
  2. Tableau workbook walkthrough
  3. Instruction of creation of your own workbooks
  4. Demo of few more workbooks

What will be the outcome for you?

Simple, you can become a data scientist. Of course, it comes only with practice and perseverance. The most important outcome is that we will put you in a structured learning path wherein even after completion of course, you can keep learning and building your profile without any confusion like you are in now.

What is mentorship included in the course?

Mentorsship will include - giving access to end to end models built already for reference. Helping students to build more projects. We will have 1 or 2 group mentorship sessions every week. The outcome is build a profile for you..

What are prerequisites for the course

Good analytical skills and knack for problem-solving, creativity coupled with commitment is prerequisite. Also, some exposure to coding is expected. You need not write an object-oriented program demonstrating inheritance but if you can write a ‘for’ loop, we can take you from there. 

Cinque Terre
Prudhvi Potuganti


Instructor-led live classes

  Date :
Batch 15
Oct 15 - Apr 5
  time :
6:30 PM - 9:00 PM IST
  type :
Live Classroom
  price :
USD 550 
Batch schedule date
  • Oct - 2021
    • Fri15
    • Fri22
    • Tue26
    • Fri29
    • Nov - 2021
      • Tue2
      • Fri5
      • Tue9
      • Fri12
      • Tue16
      • Fri19
      • Tue23
      • Fri26
      • Tue30
      • Dec - 2021
        • Fri3
        • Tue7
        • Fri10
        • Tue14
        • Fri17
        • Tue21
        • Fri24
        • Tue28
        • Fri31
        • Jan - 2022
          • Tue4
          • Fri7
          • Tue11
          • Fri14
          • Tue18
          • Fri21
          • Tue25
          • Fri28
          • Feb - 2022
            • Tue1
            • Fri4
            • Tue8
            • Fri11
            • Tue15
            • Fri18
            • Tue22
            • Fri25
            • Mar - 2022
              • Tue1
              • Fri4
              • Tue8
              • Fri11
              • Tue15
              • Fri18
              • Tue22
              • Fri25
              • Tue29
              • Apr - 2022
                • Fri1
                • Tue5

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