Data Science

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Data Science

Data Science Using Python

Course Extract
Level Intermediate
Length 7 Weekends
Projects 1 (Fully functional)
Pre-requisites None
Batch size 10 - 15
Job preparation Yes
Certificate Yes
Start date November 3, 2018

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DAY 1

DAY 2

  • Python the saviour for ML
  • Basics of Python, Installation and setup
  • Python syntax, Variable, datatypes, Keywords, Operators, Conditional statements, loops, control statements
  • Functions, LAMBDA & Modules
  • Exception Handling, File handling
  • Data structures (List, Set, Tuples, Dictionary),
  • Advanced Libraries of Python (Numpy, Pandas, Scikit-learn etc)
  • Data preparation and munging using Python libraries

DAY 3

  • Basic of Statistics & Probabilities for ML
  • Statistical Jargons, Central tendencies
  • Sample vs Population
  • Exclusive Event, Independent Event, Introduction to random variables, the Joint probability
  • Metric, Probability tree, Confusion Matrix
  • Discrete probability distributions, Continuous probability distributions

DAY 4

  • Deep dive on Linear Regression
  • Understanding Linear Regression with an example
  • Gradient descent and its parameters
  • Formulae and maths behind this model
  • Multiple Linear Regression, Polynomial Regression, Categorical Variables in Regression
  • Error metrics to calibrate performance the model
  • Hands-on modelling of 3 real-time problems (using Python and scikit-learn)
  • Pros and cons

DAY 5

  • Developing and Deploying ML models (AWS CLOUD)
  • Building a simple REST application using FLASK
  • Exposing the Linear Regression ML model as REST API using FLASK
  • Deploying the ML model in AWS and consuming it using a sample application

DAY 6

  • Deep dive on Logistic Regression
  • Understanding Logistic Regression with an example
  • Sigmoid function
  • Formulae and maths behind this model
  • Error metrics to calibrate the performance of the model
  • Hands-on modelling of 3 real-time problems (using Python and scikit-learn)
  • Pros and cons

Deep Dive

DAY 7

  • Deep dive on SVM
  • Understanding SVM with an example
  • Learning about Kernal and support vector machines
  • Formulae and maths behind this model
  • Error metrics to calibrate the performance of the model
  • Hands-on modelling of one real-time problem (using Python and sci-kit-learn)
  • Pros and cons

DAY 8

  • Time Series Forecasting
  • Understanding Trend analysis, Cyclical and Seasonal analysis, Smoothing
  • Moving averages, Auto-correlation
  • ARIMA Applications of Time Series
  • Hands-on modelling using FB Prophet for Time series forecasting (Python)

DAY 9

  • Other Algorithms in Machine Learning
  • Unsupervised Learning: Clustering techniques – K means – K means++
  • Decision Tree: Real-time use case, examples, the theory of entropy. Information gain and Gini index, Hands-on

DAY 10

  • Ensemble Technique
  • Bagging & boosting and its impact
  • Random forest, Adaboost - Gradient boosting machines

DAY 11

  • Text Mining
  • Understanding information retrieval, Crawling and Language modelling
  • Text Indexing, Inverted Indexes
  • Relevance Ranking TF and IDF
  • Evaluation Metrics for Ranking

DAY 12

  • Natural Language Processing
  • Understanding NLP, real-life systems using NLP
  • Parsing and semantic structures, Stemming, POS tagging
  • Named Entity Recognition and applications of NER
  • Sentiment Analysis

DAY 13

  • Project Week
  • Dataset will be provided
  • Expected to prepare and transforms the data to be applicable for modelling
  • Model using various algorithms and provide the best prediction results
  • Apply Ensemble techniques to improve the prediction accuracy

DAY 14

  • Project Week
  • Specific business/domain use cases will be dealt with (based on the availability of the experts)
  • Hackathon/contest
  • Certification

Course Details

Course Duration

7 WEEKS

Every Week

2 SESSIONS

Each Session

4 HOURS

Projects & Assignments

50 HOURS

=

Great Career

Mentor SpotLight

Mohamed Noordeen
Senior Data Scientist

Noordeen, with 6+ years of experience in data science and has strong experience in Python and Machine Learning. He has been coaching python, data science for quite long now.