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Introduction to Life cycle of a Data Scientist


  • Introduction Data Science Terminologies like Data Science, Machine Learning, Deep Learning, AI & Data Mining
  • Lifecycle of Data Scientist and Topic Allocation across the lifecycle & Agenda Plan Deliverables across each phase




Phase-1: Mastering Python


  • Goal Behind Python Mastering, Deliverables expected from every learner from Python
  • Basics of Python Like Install and setup, What is value, variable, function & libraries? What is an IDE?
  • Overview into Data Structures like list, tuple, set, dict, Series, Data Frame, Array
  • Practice on List, Tuple, Set, Dict, Series, Data Frame & Array
  • Deep Dive into List & Tuple
  • Practice on List & Tuple
  • Deep Dive into Set & Dict
  • Explanation on: User Defined Functions, Escape Seq, For Loop, While Loop, Selection Statements, Iterations, Python Modules(Submission Date)
  • Assignment Working and Submission(Programming Live Tasks)
  • Live Hackathon - 1 & Solution Discussion
  • Introduction to OOPS, OOPs vs POP, Types of OOPs, Types of Variables
  • Types of Methods in OOPS, Practical Implementation of all the different types of OOPS
  • Introduction to Pandas and Basic Pandas Commands
  • Practice on 10Mins to Pandas(Pandas Task)
  • Numpy Commands
  • Data Visualization Theory
  • Practice on Data Visualization with Matplotlib
  • Practice on Data Visualisation with Seaborn & Plotly
  • Data cleaning using Pandas
  • Data Cleaning using Sklearn, Label Encoding, One-Hot Encoding, Imputer
  • Assignment-1 on Data Cleaning
  • Assignment-2 on Data Cleaning
  • Introduction to Basics of Tableau like different charts & various functionalities, Filters, Actions, Maps, Dashboards & Storytelling
  • Storing Data into SQL & Integrating with Tableau & Generating Tableau Dashboard and Serving them into Tableau Server(Live Business Project)
  • Interview Question on Python




MySQL Tool


  1. Database Basics
  2. Designing your Database
  3. Data Types
  4. Creating Databases and Tables
  5. Querying Table Data
  6. Modifying Table Data
  7. Functions
  8. Joining Tables
my sql data science




Phase-2: Statistics, Probability, Time Series, Advanced ML


  • What is Data? Properties of Data like Uniform and Non-Uniform? What is Random Variable & Types of Random Variable like Continuous & Discrete Data.
  • Different Types of Analytics like Descriptive Analytics, Predictive Analytics & Prescriptive Analytics.
  • What is Sample vs the Population? Descriptive Statistics like Mean, Median, Mode, Standard Deviation Variance, Range, Quantiles, Outlier Identification
  • Normal Distribution, Skewness & Kurtosis
  • Coding to check normality, skewness & kurtosis & CL
  • What is Outlier identification/Anamoly detection & Real-time implementation of statistics
  • Understanding the concept of Correlation & Covariance, Probability theory & Different types of Probability and Probability Distributions, KDF, Sampling Distribution & CLT
  • Q-Q Plot, Chebyshev’s inequality, Bernoulli & Binomial distribution. Log Normal distribution, Power Law Distribution, Box cox transform.
  • Coding Work on Above Topics and Doubts Clarification
  • Working on Data Understanding Project with different Probability distribution coding and drawing insights out of business problems & Doubts clarification
  • Hypothesis Testing, Statistical Testing using ANOVA, 2 Sample, P-Value, Chi-Square Test, F-Statistics, Confidence Interval, Estimates
  • Covariance, Pearson Correlation coefficient, Spearman Rank Correlation Coefficient, Correlation vs causation.
  • Confidence Interval & Computing Confidence Interval. Resampling & permutation
  • What is machine learning & different types of learning like Unsupervised, Semi Supervised, Self Supervised, Reinforcement & Supervised
  • Dimensionality Reduction Techniques(PCA & T-SNE) Drill Down
  • Coding work on Hypothesis testing and different statistical testing & Doubts clarification
  • Associate Rules, Recommendation Engine, Apriori Algorithm, K-Means Clustering
  • Recommender systems & matrix factorization techniques. What is clustering & different clustering techniques
  • Project Demonstration: Solving Real-Time UseCases with Unsupervised Learning
  • Live Project Work -1
  • Time Series Forecasting using AR/MA/ARIMA/ARMA/ SARIMA/Exponential Smoothing, Markov chains, Stationarity, Trend, Cyclic, e.t.c.
  • Project Demonstration: Solving Real-Time Usecases with Time Series Forecasting
  • Live Project Work-2
  • Introduction to Regression vs Classification Algorithms Linear Regression Assumptions, Math behind OLS & Why Linear Regression, Evaluation of Linear Regression, Feature Scaling & Feature Transformation Techniques
  • Linear Regression Maths & Scripting
  • Linear Regression Accuracy Improving
  • Techniques ,Coding & how we convey the results to the client after constructing the model, Z-Values, Point Estimate, Interval Estimate, Margin of Error, Central Limit theorem, Normalization, VIF, RFE, Forward Addition, Backward Elimination.
  • Different Feature Engineering & Feature Selection Techniques which we use it based on Project
  • Practice on Linear Regression End - to - End
  • Regularisation of Linear Regression using Lasso Regression,Ridge Regression & Elastic Net
  • Linear Regression Coding using OOPs Concept
  • Linear Regression Doubt Clarification Session & Evaluation
  • Logistic Regression Math, Confusion Matrix, Classification Report, ROC Curve, AUC Curve, Variance bias tradeoff point, Odds Ratio
  • Project Demonstration on Classification Models
  • Live Project Work-3
  • Decision Tree(ID3, C4.5, CART, Greedy), Random Forest, Gradient Boosting, Ada Boosting, Hyperparameter Tuning, Grid Search
  • Coding on Hyperparameter tuning & Doubts Clarification Session
  • Coding on Hyperparameter tuning & Coding using OOPs Concept
  • End-to-End Model Deployment using Heroku, Serialization, Github, Writing backend, connecting with frontend and constructing POC
  • SVM & KNN Math Deep Dive with various Regualization concepts in SVM and importance of SVM, Hinge Loss, Different Kernals.
  • Construction POC with Valid Business Usecase
  • What is NLP, NLU, NLG Supervised NLP Techniques & Unsupervised Bag of words, word2vec, LDA, Topic Modelling, Word cloud, n-gram technique, parsing, types of parsing, entity recognition, lemmatisation, stemming, POS Tagging, TFIDF, Naive Bayes Model. Importance of LSTM in NLU
  • Live Project Work-4
Special Classes:
  • Dask for parallel computing problems
  • Cloud based ML Pipeline using Azure/AWS Sagemaker
  • Auto ML for easy ML Implementations
  • Devops Tool Chain & ML Ops
  • Deployment & Streamlit on ML & DL Models




CMLA for Indepth Data Science/AI Lifecycle & 70-80% Interview Questions


1. What are the skillset required from companies for machine learning

2. Skills evaluations for individual participants

3. Life cycle of a machine learning developer

3.1 Problem Understanding

3.2 Data collection

3.3 Data Wrangling

3.4 Choosing right algorithm

3.5 Building Model

3.6 Model Evaluation

3.7 Model Performance Improvement

3.8 Model Finalisation

3.9 Model Deployment

3.10. Model Documentation

4. Best Practices on Machine Learning

4.1 Scrum Methodology Implementation on Machine Learning

4.2 Agile CRISP Implementation on Machine Learning

5. Building Machine Learning Solution Architecture for Banking Domain Project

5.1 Data Flow Design

5.2 Data Capturing

5.3 Running Machine Learning Engine in AWS using Docker

5.4 Implementations of Jenkins for Machine Learning Deployment

6. Live Project on Machine Learning Practical

6.1 Design Business Case for Machine Learning

6.2 Designing Machine Learning Architecture

6.3 Work allocation

6.4 Data Validation

6.5 Data Understanding Phase

6.6 Domain Understanding Phase

6.7 Feature Extraction Phase

6.8 Environment Study and Algorithm Finalisation

6.9 Model Building

6.10 Model Evaluation & Deployment

6.11 Client Feedback or Stakeholder Feedback on Deployed ML Engine

6.12. Performance Improvement

6.13. Change in Tools for better deployment and better features on improvement

6.14. Machine Learning Model Release

7. How to learn different Machine Learning Tools Faster

7.1 List of tools

7.2 Google Collabs, Google ML Kit

7.3 Apache Mahout, Apache Spark

7.4 ML Deployment Eco-System Docker, Jenkins, Django, Flask

7.5. Documentation and Github Release




Phase-3: Interview Preparation Phase


  • Interview Pattern of Data Science& AI Interviews
  • Most frequently asked Interview Questions with Answers & How to explain in interviews
  • Most Frequently asked Python Interview Questions & Coding Tests & How to explain in interviews
  • Most Frequently asked Statistics Interview Questions & How to explain in interviews
  • Most Frequently asked ML/DL Interview Questions & How to explain in Interviews
  • Most Frequently asked NLP Interview Questions & How to explain in interviews
  • Preparing Data Scientist/AI Resume with Proper Roles & Responsibilities, Analytical skills & live Projects
  • How to explain about your team members, project methodology, project lifecycle e.t.c
  • How to speak in interviews based on the job description
  • How to explain tell me about project which you worked? Live Explanation over the video call to understand confidence and e.t.c
  • Interview Etiquette to follow as Data Scientist/AI Engineer
  • Resume Marketing, Linkedin Marketing, Keywords optimisation, job profile optimisation e.t.c.
  • Mock Interviews to benchmark your skill(Any number of mocks until you feel confident)




Induction Claases


  • What is Data & Importance of Data
  • Different types of Data like Scalar, Vector, point, line, square, cube
  • Different Math Equations & What is the purpose of math equation? Why to solve math equations
  • Why Linear Algebra and importance, 2D, nd, vector, row vector & column vector, dot product and angle between two vectors, Projection & unit vector, Equation of line, plane, hyper plane, plane passing through origin. Distance of point between plane, half space, equation of circle, sphere, hypershere, equation of ellipse, ellipsoid, hyperellipsoid
  • Fourier Series & it’s importance
  • Basics of Probability
  • What is statistics and importance of statistics in real-time
  • What is calculus and importance of calculus?
  • What is service industry & product based industry
  • What exactly software engineers do? What are the different roles in IT industry?
  • What is this Data Science/AI/ML/Data Mining/MLOps/Big data/Cloud? Where it going to fit in real-time? What is my job role going to be?
  • What is programming & basics of programming? How to master programming?
  • Tips and tricks to learn smartly




AI/MLOps


  • What is AI Ops
  • Different components to learn in AI Ops
  • Linux, Git, Version Control
  • Virtual Machines, Docker, Kubernetes, TFX, Kubeflow, MLFlow
  • What is Gitlab & Importance of Gitlab
  • AWS MLOps Pipeline, GCP, Azure Pipelines & Digital Ocean Pipleline




Tableau for Visualization


1. Creating visual analytics with tableau desktop

• Overview

• The shortcomings of traditional information analysis

• The business case for visual analysis

• The tableau software ecosystem

• Introducing the tableau desktop workspace

• Notes

2. Connecting to your data

• Overview

• How to connect to your data

• What are generated values?

• Knowing when to use a direct connection or a data extract

• Joining database tables with tableau

• Blending different data sources in a single worksheet

• How to deal with data quality problems

• Note

3. Building your first visualization

• Overview

• Fast and easy analysis via show me

• How show me works

· Able to represent your data using the following visualization types:

o Cross Tab o Geographic Map o Page Trails o Heat Map o Density Chart o Scatter Plots o Pie Chart and Bar Charts o Small Multiples o Dual Axis and Combo Charts with different mark types o Options for drill down and drill across

• Trend lines, Reference lines and statistical techniques to describe data

• Sorting data in tableau

• Enhancing views with filters, sort, sets, groups, bins and hierarchies

· Understand how and when to Use Measure Name and Measure Value

· Understand how to deal with data changes in your data source such as field addition, deletion or name change

Problem Statement: Connect to any datasource you are having data in it and build a report on sales and profit based on first day charts.

4. Creating calculations to enhance your data

• Overview

· Create calculations including string manipulation, arithmetic calculations, custom

Aggregations and ratios, date math, logic statements and quick table calculations

• What is aggregation?

• What are calculated values and table calculations?

• Using the calculation dialog box to create calculated values

• Building formulas using table calculations

• Using table calculation functions

• Adding flexibility to calculations with parameters

Types of parameter with example

Useful window functions

Data blending

• Using the function reference appendix

• Notes

5. Using maps to improve insight

• Overview

• Creating a standard map view

• Plotting your own locations on a map

• Replacing tableau's standard maps

• Using custom background images to plot spatial data

• Shaping data to enable point to point mapping

• Animating maps using the pages shelf or slider filters

How profits and expenses are varying across different geographical portions, Need to make a decision on how to increase the profits and decrease the expenses.

6. Advanced functions & Chart types

Overview

Using parameter to change dimensions and measures

LOD expressions

Waterfall chart

Pareto chart

Funnel chart

Control chart

Exponential Smoothing Models

7. Developing an ad hoc analysis environment

• Overview

• Generating new data with forecasts

• Providing self-service ad hoc analysis with parameters

• Editing views in tableau server

• Note

8. Tips, tricks, and timesavers

• Overview

• Saving time and improving formatting

• Improving appearance to convey meaning more precisely

• Customizing shapes, colors, fonts, and images

• Advanced chart types

9. Bringing it all together with dashboards

• Overview

• How dashboard facilitates analysis and understanding

• How tableau improves the dashboard building process

• The wrong way to build a dashboard

• The right way to build a dashboard

• Best practices for dashboard building

• Building your first advanced dashboard

• Sharing your dashboard with tableau reader

• Sharing dashboards with tableau online or tableau server

10. A Understanding and Using of all Tableau Functions

11. View reports on mobile devices

Build overview of a company across 3 years of data and project it on a dashboard and explain company overview using story telling and publish it on to Tableau Server.

12. Tableau Server

  • The Reasons to Deploy Tableau Server
  • Licensing Options for Tableau Server and Tableau Online
  • Tableau Server’s Architecture & Environmental Factors That Can Affect Performance
  • Configuring Tableau Server for the First Time
  • Data Alerts via email

13. Using Tableau Server to Facilitate Fact-Based Team Collaboration

  • Publishing Dashboards in Tableau Server
  • Organizing Reports for Consumption
  • Options for Securing Reports
  • Customized Views
  • Authoring and Editing Reports via Server Design and Usage Considerations Related to Web and Tablet & Mobile
  • Sharing Connections, Data Models, and Data Extracts
  • Embedding Tableau reports securely on the web
  • Using Subscriptions to Deliver Reports via E-mail

14. Automating Server with Tableau’s Command Line Tools

  • What do tabcmd and tabadmin do?
  • Installing the Command Line Tools
  • What Kinds of Tasks Can Be Done with Tabcmd?
  • Learning to leverage Tabcmd




Julia for Data Science


  • Getting started with installation of Julia
  • Basic functionalities of Julia
  • Data Understanding with Julia
  • Data visualization with Julia
  • Data Pre-Processing with Julia
  • Different Machine Learning Algorithms with Julia
  • End-to-end Data Science Project on Julia




R for Data Science


  • Getting started with installation of R
  • Basic functionalities of R
  • Data Understanding with R
  • Data visualization with R
  • Data Preprocessing with R
  • Different Machine Learning Algorithms with R
  • End-to-end Data Science Project on R




AWS Sagemaker Studio Deep Dive


  • Understanding the UI of Sagemaker
  • What is architecture wise difference between Sagemaker vs Sagemaker Studio?
  • Create the Dataflow in Sagemaker Studio
  • Working on Prebuilt Image and Custom Image
  • Working on Feature Store for creating feature group.
  • End to end project and Sagemaker and Sagemaker studio and understanding the difference in their architecture
  • Data Collection and Data Preparation using Sagemaker Data Wrangler, Clarify & Feature Store
  • Understanding and inspecting the parameters uing Sagemaker Debugger
  • Practical drilldown on Sagemaker Groundtruth & Experiements
  • Project Implementation and understanding the Model Registry(Versioning, Artifacts, Lineage tracking, aprroval workflow, e.t.c) while handling CI/CD in Sagemaker Projects
  • Deploy the model using Sagemaker
  • Monitoring the model and understand the data drift using Sagemaker Model Monitor.
  • Working on End-to-end project using AWS Lambda Pipeline
Explainable AI implementation using Tensorboard
Autopilot Experiments using Sagemaker
Changing the EFS Volumn based on project.





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