Data Science Modeling using Python

What is Data Science?

Every Organization has to make decisions related to their customer's requirements, Product features, Product Price, Competitors, and many more.

Data Analytics will help Organizations to make Data-driven decisions. Data Analyticsis all about different techniques and processes of analyzing raw Data to get hidden insights. It also helps businesses to optimize performance.

The flow of Data Science with Python

As a flow of Data analysis,

• Questions and assignments for which you want to answer
• Data collection
• Data preprocessing
• Data visualization
• For machine learning and modeling

These flows go back and forth. As a result of Data visualization, more Data preprocessing may be required, and further Data preprocessing may be required as a result of modeling.

1. Set the question and task you want to answer:

Define what results from you should get when performing an analysis.

2. Data collection

Then collect Data to solve the above challenges. The methods of collecting Data are roughly divided as follows.

• Use open Data statistics
• Extract Data from in-house DB
• Collect Data using web scraping and web API

Use open Data statistics

The easiest way is to use official statistics in open source. Open Data is a Data set published by public institutions for secondary analysis.

Extract Data from in-house DB

If you want to get the Data of DB, you will collect the Data by using SQL or SQL wrapper of Python.

Collect Data by web API and scraping

When retrieving Data from external websites and tools, use Web API and web scraping.

3. Data preprocessing

Even if you collect Data, it cannot be used as it is. It is necessary to process the Data according to the purpose of the analysis. Data preprocessing includes the following:

• Handling of missing values
• Convert categorical Data to continuous Data

Handling of missing values

For example, there may be missing values in the Dataset. In such cases, the overall result may be significantly distorted when performing Data analysis.

Convert from categorical Data to continuous Data

Converts categorical Data (character strings) into continuous Data for statistical analysis. Python makes it easy to preprocess the boarding port code into quantitative Data.

4. Data visualization

If you want to visualize Data in Python, you should be able to use the following modules.

• Matplotlib: Python's most major graph drawing tool
• Pandas: Data preprocessing module.
• Seaborn: Matplotlib Wrapper Library

5. In the case of machine learning, modeling

Once the Data has been preprocessed, and machine learning and deep learning are available, the final step is to model.

Benefits of Data analysis with Python

The advantages of analyzing Data with Python are as follows:

• Supports Data collection → preprocessing → visualization → modeling
• Easy preprocess large-scale Data (CSV, 1000 rows or more).
• Relatively easy to write, even for beginners

Collecting Data is quite difficult if you try to complete it with Excel alone. It's not impossible with VBA, but it may be a little heavy. Also, if you try to use preprocessing only in Excel, it will be full of functions and will be extremely heavy.

Also, compared to other programming languages (especially R), it is quite easy to understand, even for beginners. If you have a level of feeling, Python is recommended.

Who this course is for:

• Students/workers learning machine learning
• Those who find it difficult to learn various models of machine learning
• Those who are feeling the limits of statistical analysis and machine learning just by using the library
• Those who are worried about the difference between the frequency principle and the Bayesian principle
• 5. Capstone Projects helps you to implement your learning and clear your job interview with ease.
• 6. Every class your will get the class recording for your future reference.
• 7. Help you in building a profile on professional sites such as LinkedIn and Naukri.
  And many more.