{"id":162533,"date":"2023-06-22T07:33:13","date_gmt":"2023-06-22T07:33:13","guid":{"rendered":"https:\/\/www.henryharvin.com\/blog\/?p=162533"},"modified":"2024-02-15T10:36:31","modified_gmt":"2024-02-15T10:36:31","slug":"how-to-become-a-data-scientist","status":"publish","type":"post","link":"https:\/\/www.henryharvin.com\/blog\/how-to-become-a-data-scientist\/","title":{"rendered":"How to Become a Data Scientist: 5-Step Guide"},"content":{"rendered":"\t\t
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Data, Data everywhere. Today Data is the foundation of every significant decision. Based on the data, different businesses are able to decide about an individual\u2019s tastes and preferences. This in turn helps in suggestions and removing the bottlenecks. Data also influences the decisions made by individuals. The ratings for different products are the collection, analysis, and representation of data. This helps individuals and organizations in making informed decisions. As a result, the need for data and data scientists is at its peak. Everyone wants a career that has a demand and promising growth opportunities. So, in this blog, we will learn the steps of \u201cHow to become a data scientist.\u201d<\/p>\n

Data Science is the need of the hour. Data Scientists analyze and interpret the data to draw meaningful information. In the following blog, there is a 5-step guide on, How to become a data scientist. This will assist you in carefully climbing up the ladder of becoming a data scientist.\u00a0<\/p>\n

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1. Learning the Data Skills and Fundamentals<\/h2>\n\n

A strong base in mathematics and statistics is a must to play with the data. A strong and broad knowledge base is required in the collection, storage, analysis, and modeling of data. Good knowledge of Programming is necessary to work on the data. Python, R, SQL, and Excel are necessary skills to become a data scientist.\u00a0<\/p>\n\n

A strong base in mathematics, statistics, and machine learning will help in analyzing the data. Python and R helps in developing models that are easy to understand and interpret. A hands-on experience working with Tableau will assist in data visualization.<\/p>\n\n

2. To become a data scientist, Programming is the key<\/h2>\n\n

If you want to become a data scientist, then learning programming is a must. There are a number of programming languages that are used in Data Science. These are Python, R, SQL, Julia, C\/C++, etc.\u00a0<\/p>\n\n

Among them, Python is the most popular language. As per the TIOBE and PYPL index, Python has gained popularity by leaps and bounds. It is an open-source language that is not only used in data science but also in domains of web and video game development.<\/p>\n\n

R is the second most popular language among data scientists. R is also an open-source language that is specifically designed for data science. It is used in finance and academia for data manipulation and visualization. Also, it is helpful in Machine Learning.<\/p>\n\n

RStudio Server is another one of the most important tools in data science. Data Science uses a number of tools. Depending upon the need, a person can choose from TensorFlow, Apache Mahout, etc<\/p>\n\n

3. Understanding and visualizing the data<\/h2>\n\n

In order to become a data scientist, firstly we need to understand the data. SQL is one of the highly recommended tools for storing and analyzing data. It helps in preparing, wrangling, and experimenting with data. Libraries like MySQL and SQLite are used alongside Python to assist in connecting different data sources.<\/p>\n\n

When working with huge sets of data, tools like Apache Spark come into the picture. The knowledge of cloud platforms such as Google Cloud, Microsoft Azure, and AWS will prove to be a stepping stone to becoming a data scientist.<\/p>\n\n

Data visualization is another key skill needed to represent the data in the form of charts and plots. Power BI, Tableau, DataWraper, and Google Charts are the tools used for data visualization. To become a data scientist, the representation of data in an easily readable form is essential.<\/p>\n\n

R and Python have specific libraries for data visualization. Matplotlib is one such library used in Python for creating animated, static, and interactive visualizations.<\/p>\n\n

4. Gaining practical exposure through internships<\/h2>\n\n

The best way to learn is through practice. Theory becomes stronger with practical applications. Applying the knowledge helps in gaining experience in this field. Not many institutes provide internships after the course.<\/p>\n\n

Henry Harvin is an institute that provides assured internships after completing the course. Their experienced mentors give the learners an opportunity to explore the real world of data science. The 32 hours of interactive online sessions are really helpful in grasping the concepts. Guaranteed internships with Henry Harvin or Partner firms are a big plus point. It helps the learner gain confidence.<\/p>\n\n

Industry Experience during training is the cherry on the cake. It not only clears doubts but also helps in the application of tools and techniques. Projects covered are in the areas of HR, Finance, Marketing, etc.\u00a0<\/p>\n\n

Access to LMS Portal, recorded video, and CDS Certification are a few of the benefits of Henry Harvin. Monthly brush-up sessions help in the revision of the previous content. There are quite a large number of positive reviews about this course. One of the few is below:-<\/p>\n\n

5. Build a strong Profile and apply for a job<\/h2>\n\n

After gaining theoretical knowledge and practical experience, it’s time to make a smooth entry into the market. The more effort you put into your internship, the better job prospects you have. A meticulous presentation of your internship work will give you a good start to your career.<\/p>\n\n

Becoming a member of the data science community will keep you informed. It will also help you connect with the people in this area. Social media sites like LinkedIn, Twitter, etc can help you keep updated about the trends. You may also follow the influencers in your field.<\/p>\n\n

We don\u2019t have a shortage of institutes but a lack of good institutes. Henry Harvin<\/a> is one of the trusted brands that has proved its mettle. With a rating of 4.9\/5, Henry Harvin\u2019s Data Science course is a highly sought-after course.<\/p>\n\n

Conclusion<\/h2>\n\n

Data Science is a buzzing word today. Everything is data-dependent, from choosing a doctor to making a big business decision. So, the need for Data Scientists is huge. This field has a promising career for the next few decades, at least. Hence, investing in enrolling data science course\u00a0<\/a>will prove to be a good decision for the future. But, before narrowing down the institutes, good research will help you make better decisions.<\/p>\n\n

FAQ’s<\/h2>\n

Q1. What is the salary of a data scientist as a fresher?<\/h4>\n

Ans.<\/strong> As per the data, a fresh data scientist is able to bag a salary of 3-4 lakh per year. It keeps on increasing as your experience increases.<\/p>\n

Q2. Can I become a data scientist without any prior experience in this field?<\/h4>\n

Ans.<\/strong> Yes, you can, provided you have the interest and zeal to pursue a course in data science. All you need is the vigor to play with the data and statistics.<\/p>\n

Q3. How much time does one need to invest to become a data scientist?<\/h4>\n

Ans.<\/strong> It varies from person to person. It may range from 12 weeks to 12 months. But you need to have years of experience to have a stronghold in your field. Practice and perseverance is the key.<\/p>\n

Q.4 What educational background is needed for a data science course?<\/h4>\n

Ans.<\/strong>\u00a0 One should have a degree in the field of science, technology, engineering, or mathematics to enroll in a data science course. Learners from diverse fields can enter into this field but it’s important to have a basic knowledge of statistics and computer programming.\u00a0\u00a0<\/span><\/p>\n

Q.5 For what job profiles I become eligible after becoming a certified data scientist?<\/h4>\n

Ans:<\/strong> Below-mentioned jobs are available in the fields of technology, healthcare, marketing, or finance for a data scientist.<\/span><\/p>\n