Data Science probably has become a dream career for most IT professionals or aspiring tech pros. All thanks to the digital transformation which is taking place by leaps and bounds. With a tremendous increase in the amount of data that is generated every second across the globe, there is a surge in demand for data workers, including data analysts, data scientists, business intelligence analysts, data engineers, and many more.
When you look for job openings for data scientists, you will find that they are available in plenty all over the world. Job portals are flooded with data science jobs. In 2022, the list of 50 Best Jobs for 2022, released by Glassdoor, ranked Data Scientists 3rd among the positions. Due to the shortage of talented and skilled professionals, organizations are willing to pay huge salaries to deserving professionals. This is because organizations find it difficult to source and process this humungous amount of data. They need professionals who can understand business problems properly, make strategies for data analysis, and gather, clean, and organize the data so that appropriate algorithms may be applied. This way, data scientists develop recommendations to help their organizations make informed decisions.
Are you wondering how to learn data science from scratch?
If Yes, you have landed at the right place. This article will let you know the steps you should follow in order to become a Data Scientist. Also, you will know the reasons for taking a Data Science Course, which is considered the smartest move to s
Why Should I Learn Data Science?
Around 11.5 million job openings are expected to be created for Data Science roles by the year 2026. Moreover, there has been a massive 37% increase in the past three years.
In addition, data scientists are required in almost every company across every industry, including banking and finance, IT, oil and gas, media and entertainment, manufacturing, logistics, and every governmental agency.
When a career option gives you a chance to work in the industry of your choice, and that too with lucrative salaries, making a career in this domain becomes a dream for many IT professionals.
What is Data Science?
Data science deals with handling massive volumes of data to identify hidden patterns, extract useful information, and help organizations in making smart decisions, all this with the help of appropriate modern tools and techniques.
To analyze the data, you might be required to collect it from various sources. This data can be presented in different formats.
The steps used by a Data Scientist to analyze the data and come up with meaningful insights are mentioned below.
- Collecting the data: this includes processes such as data acquisition, signal reception, data entry, and data extraction. This stage includes collecting raw, structured, and unstructured data.
- Maintaining the data: This step includes data architecture, data cleansing, data staging, data warehousing, and data processing. You will take the raw data and transform it into a usable format.
- Processing the data: in this step, you will take the prepared data and also identify its ranges, patterns, and biases to identify the patterns in predictive analysis.
- Analyzing the patterns: this step may include predictive analysis, exploratory and confirmatory analysis, and more. This step forms the core of Data Science. This stage includes performing different analysis techniques as per the requirement.
- Reporting the data: this includes data visualization, decision-making, data reporting, and business intelligence. In this step, you will be required to prepare reports on the basis of your findings.
Learning Data Science from Scratch
Below are the steps you need to follow to learn data science from scratch.
- Master the basic concepts of Statistics and Maths
Statistics are at the core of data science. Many predictive models and algorithms are built on the foundation of statistics. Probability is another concept that forms one of the most important parts of Data Science.
Some of the basic concepts of statistics and probability include correlations, variance, Bayes theorem, and conditional probabilities. If you master these concepts, you will be able to obtain more meaningful results by extracting greater intelligence.
- Machine Learning
When you come across the term data science, machine learning is the first thing that you will learn. It forms the backbone of data science. You are required to have a good grasp of machine learning algorithms so that you can decide on which one to use according to the requirements.
You need to know the functionalities of databases so that you can retrieve the data and store it after performing analysis and processing. One of the most popular database query languages is SQL, and expertise in this language will allow you to store and modify data, create charts and tables, and a lot more.
In addition, you also need to understand the way relational databases work.
- Programming with Python and R
When you look for the prerequisites for data science, you will find Python at the top. R follows Python in the list. These are open-source as well as cross-platform compatible. Both languages are easy-to-learn.
You can perform any task of data science with the help of frameworks and libraries present in Python and R. both languages have different use cases, and choosing between them depends on your areas of interest.
Start with R if you like to work with data modeling and statistics. Begin with Python if you like to work on artificial intelligence and deep learning techniques.
With mathematical models, you can make calculations quickly, and predictions related to the data set you are to work on. Building data models is yet another prerequisite for data science.
If you wish to learn data science from scratch without any hassles, the smartest decision is to take up an online training course. One of the best courses is provided by an edtech giant Simplilearn which provides you with a job guarantee. The training is delivered via active industry experts with enterprise-class teaching systems. Live interactive classes and real-world projects make this course worth a try.
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