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How to Learn Data Science: Is it Still All the Rage in 2022?
The Harvard Business Review magazine’s article dubbed data scientist the Sexiest Job of the 21st Century. As per IBM, data science is a combination of math, statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning (ML). The field requires an in-depth understanding of the data being analyzed. Data scientists use programming languages to organize and extract information from large data sets. Organizations can then use this information to solve problems and make informed business decisions. So, how to learn data science and join the sexiest career of the 21st century?
Back to the Basics: What is Data Science?
What Skills Do You Need to be a Data Scientist?
You can broadly classify the primary skills of a data scientist into two categories: Technical skills, which include knowledge of programming and analytical tools, and non-technical skills, including communication skills. Over and above this, a data scientist must know:
- Machine Learning: An aspiring data scientist must also know the basics of ML. It is one of the most important aspects of data analysis and allows devices to learn as well as use past data to predict outcomes using AI. It can convert large amounts of raw data into organized trends. One can use it in various fields, from medicine to speech recognition.
- Programming Languages: Programming is the basis of machine learning and it is a vital skill if you’re interested in learning how to analyze data. As mentioned, scientists typically work with languages such as Python and R based on the data.
- Data Science Tool: Using data science tools is another essential skill required by analysts to organize raw data provided to them. Some popular data science tools include Matlab, Apache spark, and D3.js.
If you are wondering how to learn data science easily, here is a step-by-step guide for the same.
How to Learn Data Science: Step-by-Step Guide
Step 1: Find a Reason to Learn
Glassdoor has estimated that the salary of a data scientist averages around $122,499 in the US as of April 2022. With data generation increasing daily, the requirement for data analysts will rise by 31% by 2030, per the US Bureau of Labor Statistics. We were discussing how to learn data science but this could well be your answer to: why to learn data science.
Step 2: Develop the Fundamentals
One of the first steps to becoming a data scientist involves learning a programming language. Python and R are popular languages to learn, with Python being the most popular code used in industries while R, a relatively new language, is used in academia. Additionally, a background in applied mathematics makes analyzing and applying statistics to data easy.
ALSO READ: What Coding Language Should I Learn?
Step 3: Database Skills
A database management system is the software analysts use to organize, store and retrieve data. Skills used to navigate this system involve everything from data manipulation to encryption and troubleshooting. This platform allows access to organized data compiled by a business, which can then be analyzed and understood.
Step 4: Data Analysis Methods
There are multiple methods to analyze data. While some look at data to assess past events corresponding with diagnostic Analysis, others use the same data to predict future trends, synchronizing with predictive analysis. Other analysis methods involve creating statistical models to understand current events in the business or organization. A different process helps achieve each of these outcomes. It may apply either qualitative or quantitative methods.
Qualitative methods of data analysis include understanding and working with non-mathematical data. This may include videos, audio recordings, text documents, or transcripts. Several software programs are written to process this data, such as NVivo and HyperRESEARCH 2.8. Quantitative data analysis, on the other hand, involves working with numerical and other related data. These typically involve the creation of statistical models that work to find any trends in the data.
Step 5: Get Familiar with Popular Data Science Tools
You can use multiple data science tools depending on the expected results of the data analysis process. Some of the most popular ones are:
- Apache Spark: is a data processing and analytics engine that is known to handle large amounts of data. Due to this, it processes streaming content in real-time.
- MATLAB: This is short for matrix laboratory, and is typically used by engineers and scientists due to its ability to create mathematical models and visualize and analyze data.
- D3.js: Like Apache Spark, this is an open-source data science tool. It is more user-friendly than other tools, using common languages such as HTML.
Step 6: Level Up Your Communication Skills
Communication is a large part of presenting findings obtained through data analytics. With qualitative data analysis, you must understand the data before any software tools can analyze it. In addition, being able to organize results and explain them is vital. A lack of communication skills may result in misunderstandings that could hurt the business. One must note, however, that communication skills extend far beyond conversational language skills. It also comprises the creation of reports and other forms of data visualizations.
ALSO READ: How to Become a Data Scientist in 2022: The Ultimate Guide
Can You Learn Data Science for Free?
There are many online resources to learn every aspect and develop the skills required to become a data scientist. For example, learning programming languages online has become a norm, with most coders choosing to complete online courses. Similarly, understanding the concepts involved in working with data science tools and machine learning are readily available on data science courses throughout the web. However, it is always advisable to invest in a whitelabel data science course to add value to your profile and also learn the requisite skills.
Should I Learn Data Science?
The global market for data analytics will likely grow from $271.83 billion to $655.53 billion between 2022 and 2029. This forecasts a CAGR of 13.4%. As a result, there will be an increasing need for data analysts and data scientists to manage the massive amounts of data generated worldwide. With how easy it is to learn data science today, and the vast scope for growth, if you are interested in data, there’s no reason you shouldn’t enter the field. So, take your first step today. Upskill with us using our online data science courses!
By Priya Iyer Vyas
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