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Data Science Career Perspective

Many may argue, but data science is one of the hottest career options in the 21st century. In today’s technological and digital world, there are many questions that can only be answered through big data. Data science has made its mark on small companies and startups to leading MNCs around the globe.

Now, to clear your doubts regarding a career in data science, let us first understand the meaning of the term. It refers to analyzing past data and predicting future business growth with the help of deep learning and machine learning algorithms, mathematical and statistical concepts and techniques, probability, and numerous other advanced tools and technologies. The overall gist of data science is to look for hidden patterns and market trends from the given business data to make improved and well-informed business decisions for the future.

In data science, you not only need to have a good understanding of Python and SQL, but you also need to have the ability to use and combine your analytical and statistical skills to gain useful information and insights from the given business data and make necessary improvements in your organization.

You have understood in-depth what data science is and what can be done with its help. Now, you will now read about data scientists and their career paths.

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Who is a Data Scientist?

Data scientists are professionals who possess numerous skills. These professionals are a combination of statisticians, trend-spotters, mathematicians, and computer scientists. The main job role of these professionals is to decipher and decrypt large chunks of data and further analyze them to explore and extract patterns to gain valuable insights. Data scientists operate in the world of IT, business, and several other industries by analyzing complex sets of data to gain insights that can be leveraged into actions by organizations.

Data scientists generally collect and analyze business data. They do this with the help of a myriad of tools, technologies, and techniques with one goal in mind, reaching a sensible conclusion with the help of the given data. Usually, they present their organizations’ data in a visual format so that the other members of the organizations can visualize the data in a comprehensive format.

What Do They Do?

Let us briefly understand what these professionals do and what is demanded by a data science career. We will also take a look at their various roles and responsibilities.

The main role of a data scientist in an organization is to perform data mining, and data cleaning, and further represent the data with data visualization tools to the members of the organization such that they can read and comprehend the presented data. Organizations look for these professionals so that they can help store, manage, and analyze large amounts of raw organizational data, which is provided in both structured and unstructured format.

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Data Science Career Path

Data science is now a term known and used across various industries. Hence, organizations expect data scientists to have a much more comprehensive skill set and a broader understanding of various business processes. So, choosing the right career path involves acquiring the right skills, knowing the job requirements, and exploring the correct career opportunities. To kick-start your career and be an expert in the area of Data Science I would strongly recommend you to go with Intellipaat’s Data Science training in Bangalore in Collaboration with IIT Madras.

Typically, the career trajectory of a data scientist involves working across various roles such as data analyst, data engineer, business intelligence developer, and data scientist. These are considered to be the main phases or steps in the career path of a data scientist.

We will now cover the basic corporate ladder and skill set of each of these roles:

Data Analyst

Skill Set: A data analyst is skilled in data modeling, data cleaning, visualization or business intelligence, and R and/or Python programming. A data analyst also having knowledge in tools such as Tableau is highly recommended.

Corporate Ladder: As an entrant, a professional start their career as a data analyst. After gaining two to four years of experience, they go on to become an associate data analyst and later move on to becoming a senior analyst and finally a product manager with approximately nine to ten years of experience.

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Data Engineer

Skill Set: A data engineer is skilled in database management, data mining, programming, business acumen, visualization, and more.

Corporate Ladder: A data engineer starts from the junior level and goes on to become the product manager in a span of ten to twelve years, based on the skill development over these years.

Business Intelligence Developer

Skill Set: The core skill set of a business intelligence developer includes programming, visualization, data modeling, business acumen, etc.

Corporate Ladder: A business intelligence developer starts their career as a junior business intelligence developer and reaches the level of a team lead in a span of twelve to fifteen years, depending on the skill development and experience gained over the years.

Data Scientist

Skill Set: The core skill set of a data scientist includes statistics, mathematics, programming, business acumen, and problem-solving.

Corporate Ladder: A data science aspirant can start their career as a junior data scientist or any of the above-mentioned roles such as data analyst, business intelligence developer, etc. After gaining the required skill set, they can make a transition to the data scientist profile. However, one point that needs to be considered is that organizations might rename some positions as data scientists. Hence, the role and responsibilities should be considered carefully when it comes to the profile of a data scientist.

Now, let us discuss the various skills and other requirements necessary to pursue a career in data science.

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Data Scientist Job Requirements

The following are the must-have skills that you need to acquire to make a career transition to data science:

  • Learn statistics and mathematics
  • Have excellent skills in R, Java, Python, or any such programming language
  • Understand the extract, transfer, and load (ETL) concept and its process
  • Have knowledge of machine learning algorithms and tools
  • Gain experience in data wrangling, data visualization, and data exploration
  • Learn concepts and techniques of deep learning
  • Gain industry experience in big-data-related problems

To have a successful data science career, you need to have the above-mentioned skills. You also need to have working experience in real-time projects; this will act as an additional benefit for you and highlight your data science expertise when you apply for jobs. You can gain these data science skills, along with the necessary experience, by signing up for one of the best online data science courses. This holds especially true if you get trained from the best courses from Intellipaat as in these courses, besides getting trained, you will also gain hands-on experience in working on industry-based projects that will help you enhance your skills and learning experience.

You will come across the possible job growth you can have in this domain.

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Data Science Career Growth

As per the anticipation of the Bureau of Labor Statistics, there will be over 13 percent growth in jobs in the IT field between the years 2016 and 2026.

As per Indeed, there will be about a 29 percent increase in job openings for professionals in this domain this year.

Glassdoor mentions that there are over 32,500 jobs available for data scientists only in the United States.

You have read about the demands and job growth in the field of data science. Now, let us discuss the numerous job opportunities and openings available for data scientists and other data science professionals.

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Skills Required to Start a Data Science Career

Having a successful career in data science demands prominent data science skills such as data analysis, machine learning, predictive modeling, visualization, and Hadoop. To keep advancing in this field, you need to possess skills such as critical thinking, problem-solving, etc. Now, let us understand some of these skills one by one:

Hard Skills

  • Programming Languages:
    • Python: Python is a widely used programming language for data science due to its extensive libraries and ease of use.
    • R: R is another language commonly used for statistical analysis and data visualization.
  • Statistics and Mathematics:
    • A solid foundation in statistics and mathematics is crucial for data scientists. This includes concepts like probability, hypothesis testing, and linear algebra.
  • Database Technologies:
    • SQL (Structured Query Language): SQL is essential for managing and querying structured data in relational databases.
    • NoSQL: Familiarity with NoSQL databases (e.g., MongoDB, Cassandra) is valuable for handling unstructured or semi-structured data.
  • Data Visualization:
    • Data scientists should be skilled in using visualization tools and libraries (e.g., Matplotlib, Seaborn, Tableau) to present data in a clear and informative way.
  • Machine Learning:
    • Understanding machine learning algorithms and techniques is fundamental. This includes supervised and unsupervised learning, classification, regression, and clustering. To grab these opportunities one should go for Machine Learning Online Course and upskill themselves.
  • Deep Learning:
    • Deep learning involves neural networks with multiple layers and is used for tasks like image recognition and natural language processing.
  • Natural Language Processing (NLP):
    • NLP focuses on the interaction between computers and human language, enabling tasks like sentiment analysis, text summarization, and chatbots.
  • Big Data Technologies:
    • Familiarity with big data frameworks like Hadoop and Spark is essential for handling large volumes of data efficiently.
  • Cloud Computing:
    • Cloud platforms (e.g., AWS, Azure, Google Cloud) are commonly used to scale data processing and storage. Data scientists should be comfortable with these platforms.

Soft Skills

Soft skills” are often called “power skills” or “human skills” nowadays. These are abilities that are not tied to a specific job but are still very important. If you’re a data scientist, some of these skills include:

  • Communication – Being able to talk and share information effectively.
  • Storytelling – Telling a good story to explain things or persuade others.
  • Critical Thinking – Thinking carefully and making good decisions.
  • Business Acumen – Understanding how businesses work.
  • Problem-solving – Finding solutions to tricky problems.
  • Teamwork – Working well with others in a group.

These skills are valuable in many different jobs, not just data science.

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Data Science Career Opportunities

There are various job roles and titles in leading organizations for data science professionals. Some of these data science careers opportunities include:

  • Business Analyst
  • Data Analyst
  • Data Scientist
  • Product Analyst
  • Data Science Engineer
  • Analytics Consultant
  • Machine Learning Engineer
  • Business Analyst: These professionals are responsible for data extraction from various sources to perform business analysis and generate reports, dashboards, and metrics for monitoring organizational performance.
  • Data Analyst: These professionals build a cross-brand and robust strategy of data acquisition and analytics. They are also responsible for designing raw data transformation for analytical applications.
  • Data Scientist: These professionals use data analysis and data processing to understand business challenges and offer the best solutions to the organizations.
  • Product Analyst: These professionals are responsible for analyzing large chunks of data to support the product team in their product decision-making.
  • Data Science Engineer: These professionals design, build, and integrate data to build the big data infrastructure for data science.
  • Analytics Consultant: These professionals are responsible for developing and carrying out the organizations’ analytical solutions.
  • Machine Learning Engineer: These professionals, with the help of several machine learning tools and technologies, build statistical models with large chunks of business data.

Even though the above-mentioned job titles and roles are distinctive, some organizations use data scientists interchangeably with them or even rename some of the above job titles to data scientists.

Next in this blog, we will go through the salaries earned by these professionals.

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Data Science Career Salary

Data science professionals are one of the highest-earning IT professionals today. This is due to the exponential rise in their demand. However, since there is not enough availability and supply of professionals in this domain, they are generally very well paid.

According to Glassdoor, on average, data scientists in the United States earn in the range of US$113,000 p.a. to US$154,000 p.a.

In India, the average salary of data scientists is about ₹992,000 p.a. This may rise to ₹1,978,000 with expertise and experience.

So, if you wish to learn data science and pursue a career in this field, then you must register for one of the best online courses in this domain today!

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Career Transition

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Frequently Asked Questions (FAQs)

How to start a career in data science with no experience?

Begin with learning fundamental skills like Python, SQL, and statistics. Engage in online courses, build a portfolio with personal or freelance projects, and network to find entry-level opportunities.

What is needed for a career in data science?

A strong foundation in statistics, programming skills in languages like Python or R, knowledge of SQL, and the ability to interpret and visualize data are essential for a data science career.

Is Python enough for data science?

While Python is crucial, a well-rounded skill set including statistics, data manipulation, visualization, and machine learning, along with knowledge of SQL and other tools is necessary for data science.

Is data science an IT job?

Yes, data science is categorized as an IT job as it involves using technological tools to analyze and interpret data to aid in decision-making.

Should I learn SQL or Python first?

Learning SQL first is beneficial for managing databases, followed by Python for performing advanced data analysis and manipulation.

Should I learn R or Python first?

Python is recommended due to its versatility and wide application range, although learning R for statistical analysis is also valuable.

What is the minimum salary for a data scientist fresher in India?

The minimum salary for a data scientist fresher in India ranges from INR 5 to 7 lakhs per annum, varying with company and location.

Which company is best for data science fresher?

Companies like TCS, Infosys, and Wipro are good for data science freshers due to their extensive training programs and diverse project opportunities.

What is the monthly salary of a data scientist fresher in India?

The monthly salary of a data scientist fresher in India typically ranges from INR 40,000 to 60,000, depending on the company and location.

How much Python is required for data science?

Proficiency in Python basics, along with libraries like Pandas, NumPy, and Scikit-learn, is essential for data science, enabling data analysis and machine learning tasks.

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