60% Complete Courses » Python for Data Science
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Introduction to Data Science

o What is Data Science?

o What does Data Science involve?

o Era of Data Science

o Business Intelligence vs Data Science

o Life cycle of Data Science

o Tools of Data Science

o Introduction to Big Data and Hadoop

o Introduction to Python

o Introduction to Spark

o Introduction to Machine Learning

Statistical Inference

o What is Statistical Inference?

o Terminologies of Statistics

o Measures of Centers

o Measures of Spread

o Probability

o Normal Distribution

o Binary Distribution

Data Extraction, Wrangling and Exploration

o Data Analysis Pipeline

o What is Data Extraction

o Types of Data

o Raw and Processed Data

o Data Wrangling

o Exploratory Data Analysis

o Visualization of Data

Introduction to Machine Learning

o What is Machine Learning?

o Machine Learning Use-Cases

o Machine Learning Process Flow

o Machine Learning Categories

o Supervised Learning algorithm: Linear Regression and Logistic Regression

Classification Techniques

o What are classification and its use cases?

o What is Decision Tree?

o Algorithm for Decision Tree Induction

o Creating a Perfect Decision Tree

o Confusion Matrix

o What is Random Forest?

Unsupervised Learning

o What is Clustering & its use cases

o What is K-means Clustering?

o What is C-means Clustering?

o What is Canopy Clustering?

o What is Hierarchical Clustering?

Recommender Engines

o What is Association Rules & its use cases?

o What is Recommendation Engine & it’s working?

o Types of Recommendations

o User-Based Recommendation

o Item-Based Recommendation

o Difference: User-Based and Item-Based Recommendation

o Recommendation use cases

Text Mining

o The concepts of text-mining

o Use cases

o Text Mining Algorithms

o Quantifying text


o Beyond TF-IDF

Time Series

o What is Time Series data?

o Time Series variables

o Different components of Time Series data

o Visualize the data to identify Time Series Components

o Implement ARIMA model for forecasting

o Exponential smoothing models

o Identifying different time series scenario based on which different Exponential Smoothing model can be applied

o Implement respective ETS model for forecasting

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301 USD