The production and usage of data are on the rise and all companies irrespective of their size or turnover, are now dependent on data more than ever. This has led to a surge in the demand for Data Analytics and a plethora of professionals are drawn towards this field. These professionals turn to programming languages like R and Python to enhance their Data Analytical skills, but often fail to do so because of their wrong learning approach.
This article provides an introduction to Data Analytics with Python and explains why the Python Programming Language is so effective for this field. The blog will then explain the steps that you should follow if you wish to start from scratch and become efficient in Data Analytics using Python. Furthermore, it will discuss the common mistakes that you must avoid while on this learning journey. Read along to learn more about Data Analytics with Python!
Prerequisites
- Working knowledge of Maths and Statistics.
- Basic understanding of Data Structures.
- Basic understanding of Data Types.
- Basic understanding of Programming concepts.
Introduction to Data Analytics with Python
Data Analytics involves collecting data from various sources and using Statistical Analysis and Machine Learning on that data to extract valuable insights from it. It is a popular concept, especially in the commercial sector as it allows organizations to make data-driven decisions based on the result of Data Analysis.
Nowadays, Data Analytics and Python are two inseparable terms. The popularity that Python has witnessed in the field of Data Science and Data Analytics is because of its immense flexibility and functionality. Moreover, to implement Data Analytics with Python, you don’t have to learn everything about the programming language. Since you won’t be doing the development work, understanding certain libraries and functions offered by Python is sufficient.
Also, you must develop your Data Science skills, otherwise, learning Python will be like having a tool and not knowing how to use it. Therefore, you need to develop some Statistics and Data Visualization skills and gain a certain degree of knowledge about the subject area to be extracted and analyzed.
Steps to Learn Data Analytics with Python
Data Analytics can appear to be a complex process for a beginner, but you can easily understand the important aspects of implementing Data Analytics with Python by working along with the following steps:
Step 1: Set Up a Python Environment
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The basic necessity to work in Data Analytics with Python is to have a platform where you can write your code and execute it. So, your first step is to set up an environment that is convenient to use and enables you to work in Python. There are multiple platforms available online for free that can provide you the required programming environment, the most popular being the Anaconda Python Platform. This one application will take care of most of your needs as it contains along with the core Python Programming Language, most of its important libraries such as Pandas, Numpy, Matplotlib, IPython, etc.
You can download the Anaconda Package and install it on your system just like any other application. The package has various in-built programs one of which is the Jupyter Notebook. It will act as a well-developed environment for working in Python and will enable you to compile and run your code seamlessly. Jupyter Notebook will open in your browser and won’t require any internet connection to execute your code. Once this installation is complete, your environment is ready!
Step 2: Learn the Basic Concepts of Python
It is essential that you first understand the fundamental concepts of Python before jumping into any kind of Data Analytics with Python. You don’t need to become an expert in this programming language, just covering the following important topics will suffice:
- Implementing Data Structures
- Learning the Various Data Types
- Creating Functions
- Using Loops
- Using Conditional Statements
- Working with Imports
Furthermore, you don’t need to enroll in any course to learn all of the above concepts. There are multiple resources like W3Schools, Tutorials Point, etc. available for free on the internet that provide detailed tutorials on Python fundamentals in the form of videos, notes, etc. Learning these concepts will provide you the required foundation to start your Data Analytics with Python.
Step 3: Understand the Working of Python Libraries
A key feature of Python is that it has numerous libraries that can simplify your work to a great extent. If you want to perform Data Analytics with Python, then you must familiarize yourself with some of the majorly used Python Libraries. The essential Python Libraries with regards to Data Science are:
- Pandas: It is the most important Python Library when it comes to Data Manipulation and Data Analysis. Due to the presence of Data Manipulation tools and high-level Data Structures, it is ideal for Data Cleaning and Data Manipulation, both of which are the basic tasks of any Data Analyst. It supports a Data Structure called Data Frame that is exceptionally good to store data in a tabular format. Furthermore, Pandas allows you to clean your messy data, fill out any kind of missing values and implement other aspects of Data Preprocessing.
- Numpy: This Python Library provides strong computation tools that can streamline your Mathematical and Statistical Operations when you are implementing Data Analytics with Python. Numpy is the most fundamental Python Library. Pandas is an extension of Numpy only. The main reason for Numpy’s fast scientific computation power is that it contains Multidimensional Arrays which are specially optimized for calculation work involved in Machine Learning algorithms.
- Scikit-learn: This is your go-to Python Library when you wish to implement any kind of Machine Learning model. If you are applying Data Analytics using Python, Scikit-learn can automate the process of extracting valuable insights from a large amount of data. Moreover, it allows you to create models using Machine Learning algorithms to predict future trends and results. This library is ideal for Data Mining work too as it gives you an efficient interface to work with various Machine Learning models.
- Matplotlib: This library encompasses features that allow you to visualize your data using various graph-based representations. Matplotlib provides you total control over these graphs. You can modify the Colors, Shapes, Axis, Style, Thickness, Range, etc. of your visual plot.
These 4 Python Libraries are a must if you wish to work on Data Analytics with Python. Once you have understood these, you may try and explore other important libraries to further increase your knowledge of implementing Data Analytics with Python. These libraries and many more are pre-installed on your Jupyter Notebook. Still, if any library is absent, you can easily install it using the pip command.
Step 4: Practice Working with Datasets
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The above 3 steps were aimed at learning certain tools and techniques that will facilitate your Data Analytics with Python. Now, it is time to implement this knowledge on actual Datasets. There are enough Datasets present in the StatsModels Libray in Python, and you can also download more from platforms like Kaggle for further practice. By applying basic Statistical and Analytical operations on these Datasets, your confidence in Data Analytics and Python will increase and you will realize the areas in which you need to improve. On these Datasets, you must practice the following 4 types of processes:
- Data Cleaning: It involves finding and correcting any inaccuracies or ambiguity present in the stored data.
- Data Preprocessing: It is the process of modifying data into formats that are more suitable for performing Data Analytics with Python.
- Data Manipulation: It is the process of implementing Machine Learning models on data to obtain desired results. Tasks like Clustering, Classification, Regression, etc. fall under Data Manipulation as shown in the below image.
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- Data Visualization: Results obtained by any of the above 3 processes of Data Analytics with Python are represented in a more understandable manner using Data Visualization. It includes Bar Graphs, Pie Charts, Heat Maps, etc. as shown in the below image.
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Mistakes to Avoid in Data Analytics with Python
Most beginners often commit the following mistakes while learning Data Analytics with Python:
- Learning Excessive Theory: Most people when starting Data Analytics using Python, tend to focus more on the theoretical aspects of the language. They focus on learning the theory of Machine Learning algorithms rather than going for practical experience. This theory-based approach will slow down your learning and may be overwhelming for some people. This can lead you to give up early in your preparation.
- Learning Complex Algorithms at Early Stages: In the initial stage, you don’t need to learn complex Machine Learning algorithms from scratch. Beginners often have a perception that perfecting numerous complex algorithms early will give them a competitive edge. However, this mistake must be avoided as it will not allow you to build a strong basic foundation of Python Programming Language, and rather than practicing multiple algorithms, it’s more important to understand which algorithm should be applied under what circumstances.
Instead of falling into the trap of these mistakes, focus on learning at your own pace. Furthermore, focus on practical implementations of Python Libraries and simple Machine Learning algorithms.
Learn More About:
A Guide to Effective Data Cleaning Tools in Python
Conclusion
This article discussed the learning process of Data Analytics with Python. It explained the various steps that you should follow to succeed in your attempts to implement Data Analytics using Python. Moreover, it highlighted the common mistakes that you must avoid while starting your learning process. Although Data Analytics is a complex field, the inbuilt functions and libraries in Python can simplify it for you. All you need to do is follow the steps provided in this article and practice as much as possible.
Abhinav Chola, a data science enthusiast, is dedicated to empowering data practitioners. After completing his Master’s degree in Computer Science from NITJ, he joined Hevo as a Research Analyst and works towards solving real-world challenges in data integration and infrastructure. His research skills and ability to explain complex technical concepts allow him to analyze complex data sets, identify trends, and translate his insights into clear and engaging articles.
