Welcome! Folks to Become a Data Analyst Course.

You will Learn python basics via Jupyter Notebook, So Let's Get started.

What is Python ?

Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable. It uses English keywords frequently where as other languages use punctuation, and it has fewer syntactical constructions than other languages.

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  Python is Interpreted − Python is processed at runtime by the interpreter. You do not need to compile your program before executing it. This is similar to PERL and PHP.
  
  
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  Python is Interactive − You can actually sit at a Python prompt and interact with the interpreter directly to write your programs.
  
  
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  Python is Object-Oriented − Python supports Object-Oriented style or technique of programming that encapsulates code within objects.
  
  
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  Python is a Beginner's Language − Python is a great language for the beginner-level programmers and supports the development of a wide range of applications from simple text processing to WWW browsers to games.
  
  

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  Easy-to-learn − Python has few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language quickly.
  
  
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  Easy-to-read − Python code is more clearly defined and visible to the eyes.
  
  
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  Easy-to-maintain − Python's source code is fairly easy-to-maintain.
  
  
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  A broad standard library − Python's bulk of the library is very portable and cross-platform compatible on UNIX, Windows, and Macintosh.
  
  
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  Interactive Mode − Python has support for an interactive mode which allows interactive testing and debugging of snippets of code.
  
  
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  Portable − Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
  
  
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  Extendable − You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.
  
  
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  Databases − Python provides interfaces to all major commercial databases.
  
  
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  GUI Programming − Python supports GUI applications that can be created and ported to many system calls, libraries and windows systems, such as Windows MFC, Macintosh, and the X Window system of Unix.
  
  
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  Scalable − Python provides a better structure and support for large programs than shell scripting.
  
  

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  It supports functional and structured programming methods as well as OOP.
  
  
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  It can be used as a scripting language or can be compiled to byte-code for building large applications.
  
  
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  It provides very high-level dynamic data types and supports dynamic type checking.
  
  
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  It supports automatic garbage collection.
  
  
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  It can be easily integrated with C, C++, COM, ActiveX, CORBA, and Java.

In this Chapter , we will learn how to use

All the Topics Covered under this Chapter are the Basic Use-cases of Numpy usage in daily usage.

• Introduction to Numpy
• Some basic Examples
• Numpy Arrays: Creation, Methods & Attributes
• Common Manipulations: indexing, slicing & reshaping
• Examples: Performing a Simulation

By Learning the implementation of this use-cases we will learn and demonstrate these project by the end of course.
Shown below.

Now here you will Learn about: Analysis with Pandas

• Introduction to Pandas (Docs)
• Introducing the most important objects: Data Frames and Series.
• The Pandas Series
• Creating a Pandas Series
• Pandas DataFrames
• Main Properties , Operation and Manipulations
• Reading the data
• The Anatomy of Data Frame
• Inspecting the Data - Selection , Addition , deletion
• Slicing
• Answering Simple Question about the dataset ?

Congrats! you have Learnt a lot 
Now it's Time to Learn about the Seaborn

• Matplotlib

1. The basic Concepts: figures, subplots ( axes) and axis.
2. Object Oriented Interface (OOI) vs pyplot
3. Common Customization: colors, labels , ticks, tick marks, limits and annotations
4. Some Commonly used visualizations in data analytics.

• Introduction to Seaborn

• Exploratory Data Analysis

1. Seaborn
2. What is Exploratory Data Analysis(EDA)
3. Doing Exploratory Data Analysis: Loading the data
4. Some Questions that will Guide you the Data analysis being done.
5. Understanding main ,Numerical ,Categorical variables.
6. Relationships between Numerical variables
7. Relationships Sales Price with Categorical variables.

In this Section , we are going to learn about

Scipy-Statistics.

1. The Statistics Sub-package

Project: Alcohol Consumption of students

• Confidence Intervals
• Probability calculations
• Null Hypothesis testing Framework
• Some Statistical test from scipy.stats
• Basic Question based on Project Alcohol Consumption of students . (BASED ON REAL USE-CASES)

So , Far you have learnt about Numpy , pandas, scipy-stats, Matplotlib and Sea-born.

Let's get started with the final usage or you can say building the Regression & Classification Model out of it.

Analysing the House Predication Data sets :

• For Regression Example:
• Analyzing Neighbourhood with more than 30 Observations
• Transforming Neighbourhood and CentralAir to one-hot Encoding format
• Building a Linear Regression Model
• Make a prediction for New house.

Analyzing Predicting the drinking habits of teenagers

• What is simplest possible model ? just predict the most common category!
• Building Logistic Regression Model
• Model Evaluation (naively done)
• Accuracy of Logistic regression
• Some Complex Models examples

Analyzing the Scikit-learn with example data-sets

• Getting some data

• Estimators objects

1. Import the estimator (model)
2. Create an instance of the estimator
3. Use the data to train the estimator
4. Evaluate the Model
5. Use the data to make "Predictions"