BEGINNER-PYTHON FOR DATA ANALYSIS

Who is a Data Analyst? A data analyst is responsible for interpreting data and analyzing the results utilizing statistical techniques and providing reports. They develop and implement data analyses, data collection systems, and other strategies that optimize statistical efficiency and quality. They are also responsible for acquiring data from primary or secondary data sources and maintaining databases.

Imagine having to find the average BMI of 5000 people to determine the chances of them getting a Type 1 diabetes in the next 10 years or determining the rate of population explosion from 2010 to the present year! One thing is common in the above scenarios LOTS OF RAW DATA. Now imagine finding a way to classify and arrange data with a system that makes it all of a sudden make more sense and provides clarity. The above scenarios are significant to the reason why a system for data classification is important.

WHAT IS PYTHON

PYTHON is an interpreted, high-level, general-purpose programming language. General-purpose should stand out because python is used in many fields to achieve many results but for the purpose of this article, we’ll stick to the use of python for data analytics. In technical terms, Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options. Python is dynamic and it supports both Structured programming (here the program is made as a single structure to improve the clarity of programs) and Object-Oriented Programming (here data type of the data structure, as well as the functions for the structure, is defined). Python is relatively simple, so it’s easy to learn since it requires a unique syntax that focuses on readability. Developers can read and translate Python code much easier than other languages. In turn, it allows teams to work collaboratively without significant language and experience barriers.

Python is the language to process text, display numbers or images, solve scientific equations, and save data. In short, it is used behind the scenes to process a lot of elements you might need or encounter on your device(s) (mobile included).

WHY PYTHON?

For data analysis, python presents a more general approach and provides data frame(s), and tools to explore and make meaning of data. Python for data science is widely used because of its flexibility and open-sourced language. It has massive libraries for data manipulation and is very easy to learn even for a beginner data analyst. Python is a well-supported language because of its use for web and desktop applications and users all over the world always share their experience and provide more libraries that can be useful to newbies and anyone willing to learn. Python is a valuable part of the data analyst’s toolbox, as it’s tailor-made for carrying out repetitive tasks and data manipulation, and anyone who has worked with large amounts of data knows just how often repetition enters into it. By having a tool that handles the grunt work, the data analysts are free to handle the more interesting and rewarding parts of the job. Machine learning is the next most common word you will hear of when talking python but before all that fancy machine learning you will have to have processed your data thoroughly. Most thriving companies depend on the ability to train models that make sense of data received or sent out on a daily basis.

GETTING STARTED

Anaconda is the open-source scientific python distribution aimed at simplifying the python package to create an interface and system for easy and fast computing. It is the most popular and standard platform for python data science. Anaconda provides the tools needed to; collect data from files databases and data lakes, manage environments, share, collaborate on, and reproduce projects as well as deploy projects into production with the single click of a button. Statistical analysis and data visualization are the foundation Anaconda is built. Anaconda is popular because it brings many of the tools used in data science and machine learning with just one install, so it’s great for having a short and simple setup. Anaconda also uses the concept of creating environments so as to isolate different libraries and versions.

I bet you couldn’t miss the wordplay; ‘Python’ first then a package for it called ‘Anaconda’. It is the next greatest invention since data science because you climb to the top of a mountain load of data in minutes or hours by just getting your syntax right. You will get errors, yes, but every great story starts with a fail so its perfectly okay to mix up things at first.

The next thing to know is the python libraries incorporated with the Anaconda distribution that make computing and scientific calculations so easy.

Python Library for Data Science

A python library is a collection of functions and methods that allows you to perform many actions without writing your code. So think of a python library as an easy way to bypass long lines of code with short already defined syntax. The most common and top 5 Python libraries for the purpose of this article as a beginner backed by python developers survey are;

1. Numpy- is the fundamental python library with the most used cases for machine learning. The Numpy library is for scientific computing and for simplification of complex mathematical operations. It is highly interactive and provides the array interface every data scientist needs.

2. Pandas- is the easy to go library for combining, grouping and filtering data. It is most popular because of its numerous in-built functions which make you write less code. It is fast and makes the process of data manipulation easier.

3. Matplotlib- just like you could take a guess from the name Matplotlib is a plotting library for the Python programming language. It is a data visualization library that makes the plotting of arrays easier possible. Data is represented in graphs, histograms, charts, etc.

4. SciPy- is the python library that provides modules for integration, linear algebra, optimization, and statistics. SciPy supports signal processing and handles mathematical operations related to machine learning easier and faster.

5. Scikit-Learn- the primary intent for this library is data mining and data analysis. It is the foundation for Numpy, pandas, Matplotlib, and SciPy. It offers a wide range of algorithms for machine learning tasks, regression, and data modeling.

An in-depth study of python for data analysis will show other areas like Machine learning, Artificial Intelligence, and Deep Learning. Think of these areas like the ways you can train a computer with data frames and tools to do certain things you require of it. Data analysis will be appreciated when a trained model understands what to do with a few command instructions and can execute the commands correctly. We will only delve into the beauty of machine learning and artificial intelligence after we have grasped the basics of data analysis.