Most aspiring data scientists learn Python by taking developer-oriented programming courses. You also begin to solve Python programming riddles on websites such as LeetCode, assuming that you need to improve programming concepts before analyzing data using Python. It is a significant mistake because data scientists use Pythons to retrieve, clean, simulate, and construct models, do my python homework, not to create software applications. Therefore you can work on studying Python modules and libraries to carry out those activities most of the time. Take the gradual steps to review computer science Python (Ahmad, 2019).
Table Of Contents
Configure the programming environment
The Jupyter Notebook is a versatile programming platform for data science project creation and presentation. Anaconda is the fastest way to run Jupyter Notebook on your computer. Anaconda is the most basic Python Data Science Distribution and is preloaded from all popular libraries. We can learn how to use Anaconda in the blog post called “A Beginner’s Guide to Installing Jupyter Notebook Using Anaconda Distribution” Choose the new Python 3 update when you launch Anaconda. After the Anaconda download, you can learn how to use Jupyter Notebooks in this article about Code Academy.
Learn just the basics of Python
The Code Academy takes about 20 hours to complete an outstanding Python course. It would help if you did not upgrade to the Pro version because your goal is to get to know Python’s primary programming language.
Excellent source
Python is sluggish to process vast volumes of data with numerically heavy algorithms. Then why is Python the most popular data science programming language? The response is that it is easy to download C or FORTRAN extensions into the lower level in Python. That’s what Numpy and Pandas are doing. It would be best if you practiced Numpy first. It is the most significant module for Python’s science computing. Numpy provides support for the most simple data structure on most machine learning algorithms for optimized multidimensional arrays. You’re going to learn Pandas next. Data scientists use much of their time to clean the data, often referred to as data munging. Pandas is Python’s most common data handling library. As an extension of NumPy, Pandas is. The codes of Pandas are used widely in the NumPy library. A data frame is the principal data form in Pandas. Wes McKinney, developer of Pandas’ book “Python for Data Analysis,” has written amazingly (Butwall, Ranka, & Shah, 2019).
Learn to visualize data using Matplotlib
Python’s essential package to build simple visualizations is Matplotlib. It would be best if you learned to build some of the most popular charts, such as line charts, bar charts, scatter plots, histograms, and box plots using Matplotlib. Seaborn is another healthy plot book on Matplotlib that is tightly integrated with Pandas. I would recommend that you learn how to construct simple charts in Matplotlib and not how to concentrate on Seaborn.
Use SQL and Python
Data remains in a folder in businesses. You thus need to know how to recover data via SQL and analyze it using Python in the Jupyter Notebook. Data scientists use both SQL and Pandas to manipulate the data. Since there are unique data processing tasks that can be quickly handled with SQL, those tasks can be effectively performed with Pandas. I want to use SQL personally for data extraction and Pandas manipulation. Nowadays, enterprises use computational tools such as fashion analytics and data bricks to quickly work with Python and SQL. You should, therefore, help with python programming, know how to use SQL and Python together effectively. To understand, you can use Python and SQL to create an SQLite database on your machine and save a CSV file there and study it.
Statistics with Python
Many inexperienced data scientists go straight to the machine without learning statistics. Don’t be mistaken, as numbers are the foundation of data studies. Data scientists who study statistics, on the other hand, learn only the theory, not realistic principles. I say you should know, with practical principles, what kind of statistical problems can be solved. Understand what obstacles numbers can solve. Sampling, distribution of frequencies, mean, center, mode, a measure of uncertainty, probability fundamentals, mean measures, standard deviation, z points, trust intervals, and test hypotheses (including A/B tests). Many people say that Think Stats learn Python stats, but using standard Python modules, such as the Stats templates, the author teaches his custom functions (Raschka, Pattersonv, & Nolet, 2020).
Perform Machine Learning using Scikit-Learn
Scikit-Learning is one of Python’s most popular machine research libraries. Your purpose is to learn how to use Scikit-Learn to apply some of the most frequent learners’ algorithms.
Conclusion
Your final move is to carry out a data science experiment addressing all of the above. The data collection you want to find, and then exciting market questions can be answered by the study. But don’t use generic data sets for your project like Titanic Machine Learning. Another approach is to use data science in a profession you enjoy. For example, you can scratch and stock prices using Machine Learning to forecast stock prices in real-time and store data on Yahoo Finance in a SQL database.