Today, data analysis is an essential skill for humans who want to succeed in the modern world. You can use Data Analysis to understand and interpret the data differently. Furthermore, it provides insight into the business decisions you make, as well as helps you improve the processes you follow within your organization. The purpose of this blog post is to show you how you can become a Data Analyst in 24 hours by following these simple steps.
How many would like to work on Data Analysis as a job?
There has been an increase in jobs in the data analysis field over the last few years. It is no secret that the popularity and significance of data science have increased over the previous few years, and many companies are looking for new ways to incorporate data within their operations.
It is possible to utilize data analysis for various purposes, such as in the marketing and finance sectors, but also in the sports or education sectors.
Before choosing a course at university, you should be clear about what career path you want to pursue after graduating. Some courses are better suited to a particular career path than others, depending on the type of profession you intend to pursue after graduation.
What skills do you need for this type of job?
Considering that this is a job where you must perform a wide range of data analytics tasks, you should be able to complete them all. To do so, you will need skills like:
- Python programming language (a popular choice)
- In many programming languages, Pandas, NumPy, and Matplotlib are open-source libraries capable of plotting graphs and performing other tasks. Their versatility makes them quite useful for a wide variety of functions.
- The main idea behind web scraping with Python’s urllib2 Module (or by installing cURL) is to provide you with the capability of doing it even without accessing the internet. It does not require any specific expertise in HTML structures but instead only a basic understanding of how it works.
- A fundamental introduction to SQL, including essential concepts such as SQL query syntax and database statements, as well as an exploration of SQLite’s functionality, accessible through various commands found in the command prompt, such as ‘SQLite3’.
Step 1: Python
A popular programming language that is beneficial for data analysis and web development and is a general-purpose language. The reason why this language is so popular among both programmers and coders alike is that it is generally viewed as among the most well-known and widely used programming languages available at the moment as a result of its popularity and wide use within the industry.
While Python is a programming language that has been around since 1991, it was in the year 2000 that the source code was made open-source (which means anyone can download or modify the source code), and it became available to everyone.
For Mac OS X 10.9 Mavericks or later versions, you’ll notice that they use Python 2.6x instead of the latest Python iteration, Python 3.0. Prior versions of Python are available, as well. For instance, if you’re using Mac OS X 10.9 Mavericks or a later version, you’ll find Python 2.6x in use instead of Python 3.0.
However, there are many ways to use Python for data analysis:
As a developer, you can write your scripts using the basic syntaxes needed; however, this method might not work well if your program needs more advanced features like loops and the like, so unless otherwise required, consider using one from a third-party script.
Step 2: Pandas
This library is a Python package that provides a variety of data structures and statistical functions to work with tabular data using the Python language.
It can analyze a wide variety of different types of data, from time series (e.g., stock prices) to relational databases, NoSQL databases, social networks, etc.
There are many advantages to using Pandas compared to other libraries like Matplotlib or Seaborn: it is easier to learn because you don’t have to write any code; it is faster than other libraries due to the use of the “pandas-core” engine; OpenMP supports it for parallel processing; and so much more.
Step 3: Numpy & Matplotlib
In this step, we will import Numpy and Matplotlib.
- Import numpy as np
- Import matplotlib, globals() as g
Step 4: Web Scraping
- Import requests.
- Import lxml.
- Import urllib.
- Import BeautifulSoup.
- Import json.
It is now time to start scraping our website’s data: requests – this is the tool we will use to make HTTP requests; lxml – to parse XML documents; urllib – to retrieve information from a URL (we will use it later); BeautifulSOUP – create a list of links on a website by parsing its HTML code, pd as pandas to import data into the programmatically created dataset using the pandas idiom; finally, will use a request again to return the data into Pandas (using the Pandas idiom).
Step 5: SQL Basics.
This concept is called SQL (Structured Query Language), a tool for organizing data in a database system. It is the language used to develop, read, and update data in a relational database. SQL stands for Structured Query Language.
SQL Basics:
- What is SQL?
- How does it work?
- What can I do with it?
Bonus step 1. GitHub and version control.
The first step is to get a GitHub account. You can create one here: https://github.com/signup/
After you have created an account, click on “GitHub” in the top right corner of your new dashboard and select “New Repository.” By doing this, you can create new repositories for your data analysis projects and use them as you need.
Once you have created repositories, you can store your data analysis projects. GitHub repository is a folder on your computer where all of your code lives and is stored online for others on GitHub to see at any time if they decide they want access to it.
Bonus step 2. Machine Learning in Python.
You’re probably wondering, “What is Machine Learning?” Well, it is a subfield of computer science that developed out of the studies of recognizing patterns and the theory of computational learning in artificial intelligence.
Machine learning is analyzing data and making predictions based on that data using computers. The idea behind machine learning dates back to 1952 when John McCarthy coined the term “artificial intelligence” (AI), now known as artificial intelligence.
During the same year, Arthur Samuel published his book “Introduction to Mathematical Programming,” which introduced us to various mathematical programming concepts, such as linear programming and quadratic programming. They are still relevant today because they have applications across multiple industries, including healthcare management systems and supply chain optimization.
Now, you can apply to companies or start your data projects.
Now that you’ve learned how to analyze data, it’s time to use your skills. You can apply them in any number of ways:
- Get a job in the field of data science. A data scientist is a professional who works on projects. They use statistics and machine learning to help companies make more informed decisions by leveraging big data and analytics. Consider a career at a company that uses a large amount of statistical analysis as part of its product development process if you have strong quantitative skills and would like to use them in your career. Consider companies like Google or Facebook that use a lot of statistical analysis to develop their products.
- It’s a good idea to get others involved in your project by crowdsourcing (sharing the code) or open-sourcing (sharing the code freely) so that other people can improve it and contribute to the original design. For example, suppose someone creates an app that uses artificial intelligence (AI) algorithms based on machine learning techniques. In that case, this person might share all their code online so other developers can build upon those ideas instead of having them reinvent everything from scratch again whenever there’s some improvement needed; this way, everyone benefits!
Conclusion
Today’s society relies heavily on the ability to make sense of data, and this is a valuable skill that has many career opportunities related to career development. If you have the skills needed, whether you want to work for a big company or start your own company, everything will work out for you as long as you have the skills necessary to succeed.
