Data Science on Python

Python, the programming language, is considered the Swiss Army knife of the coding world. Unlike programming languages like R, it supports structured programming, functional programming patterns, and object-oriented programming. Python is an all-in-one, unified language capable of handling running embedded systems, data mining, and website construction.

Python becomes Pythonic when the code is written naturally. It has many other features that attract the data science community. Being a data science tool, Python helps to explore the concepts of machine learning in the best way possible. The reason for growing success of Python is the availability of data science libraries for aspiring candidates. Machine Learning is all about probability, mathematical optimization, and statistics, which are all made easy by Python.

Main Features

  • Review common Python functionality and features along with Jupyter Notebook
  • Learn about the toolkits Python has for data cleaning and processing — pandas.
  • Create stunning data visualizations with matplotlib, and seaborn
  • Learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates.
  • Get Introduced to a variety of statistical techniques such a distributions, sampling and t-tests using real-world data
  • Involve yourself into data cleaning activity and provide evidence for (or against!) a given hypothesis.
  • Performing dimensionality reduction techniques like Factor analysis and Cluster Analysis.
  • Learn how to perform predictive modelling using Python.
  • Gain intensive knowledge in the spheres of Linear Regression, Logistic Regression and Time Series Regression using packages like Pandas, Numpy, scikit learn and others.

Getting Started with the most popular Data Science Library: Pandas

1
Introduction to the pandas library
2
Data structures in pandas
3
Series
4
DataFrames
5
Different applications and functions of Pandas
6
Sorting and filtering
7
Apply

Working with Pandas

1
Imputing missing values
2
Pivot tables
3
Crosstabs
4
Merge DataFrames
5
Sorting data frames
6
Plotting with Pandas
7
Analysis broken down into various steps
8
Exploratory Analysis
9
Basic Descriptive Statistic Analysis
10
Distribution Analysis
11
Categorical Variable Analysis
12
Data Munging
13
Treating Missing Values

Building Predictive Models

1
Linear Regression Theory
2
Understanding Regression
3
Training and Validation
4
Goodness of Fit
5
Practical Application
6
Exploratory Analysis
7
Case study
8
Logistic Regression Theory
9
Linear Probability Model
10
Concept of Classification
11
Comparison with Linear Regression
12
Odds Ratio
13
Classification Table
14
Practical Application
15
Getting the data
16
Reading the data
17
Algorithms
18
Decision Tree
19
Random Forest

Time Series Analysis

1
Loading and handling data
2
Checking Stationarity
3
Differencing
4
Detrending
5
Forecasting
6
ACF
7
PACF
8
AR
9
MA
10
ARIMA
11
Case study based application
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Enrolled: 34 students
Duration: 60 hours
Lectures: 50
Video: 9 hours
Level: Intermediate

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Working hours

Monday 9:30 am - 7.00 pm
Tuesday 9:30 am - 7.00 pm
Wednesday 9:30 am - 7.00 pm
Thursday Closed
Friday 9:30 am - 7.00 pm
Saturday 9:30 am - 7.00 pm
Sunday 9:30 am - 7.00 pm
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