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Data Science vs Machine Learning vs Data Analytics: Expert Discussion

Data Science vs Machine Learning vs Data Analytics: Expert Discussion

The career opportunities and future advancements in data science, data analytics, and machine learning are enormous. Organisations seek to hire professionals who are experts in these fields.

While the three are interconnected, people follow different approaches and produce varying results. So, with considerable doubt still hovering around Data Science vs Data Analytics vs Machine Learning, it is time to explore and leverage them.

At Newton School, we provide expert support and learning materials to students interested in these domains. We are a comprehensive platform offering you guidance, strategies, and tips to help students ace top-level interviews.

 

What is Data Science?

In today’s age, ‘data’ is the biggest asset for any business. On average, about 2.5 quintillion data bytes are processed daily, enabling tech experts to gather insights and discover patterns that facilitate better decision-making with access to more data.

Data science is a multidisciplinary concept that focuses on extracting insights from larger data bowls. It tackles raw and structured big data to store larger data volumes using machine learning techniques to build predictive models. This in-depth analysis and extraction of insights from data aid in strategic business planning for conversion into tangible value.

Data Science Lifecycle:

  1. Capturing: Gathering structured and unstructured data using acquisition, signal reception, extraction, and data entry.
  2. Maintaining: The stage covers data warehousing, data staging, data cleansing, processing, and data architecture to put all data into a form.
  3. Processing: Experts use data mining, modelling, summarisation, and classification to examine data patterns and ranges for predictive analysis.
  4. Analysing: Predictive analysis, quantitative analysis, regression, and text mining help perform different analyses on the collected data.
  5. Communication: In the final step, data reporting and visualisation techniques help convert the analysis into charts, graphs, and other readable formats.

Skills Needed to Become a Data Scientist

To become a successful data scientist, the aspirants must excel in the following:

  • Programming languages like SQL, Python, Scala, and R Programming
  • Mathematics like Statistics, Geometry, Calculus, and Linear Algebra
  • Data Analysis and Visualization
  • Web Scrapping
  • Big Data and Machine Learning
  • Problem-solving and Analytical skills

Career Options in Data Science (India) with Average Salaries

  • Data Scientist - ₹6,98,413
  • Data Analyst - ₹4,19,465
  • Machine Learning Engineer - ₹6,91,892
  • Applications Architect - ₹1,937,856
  • Machine Learning Scientist - 7.2 LPA
  • Data Architect - ₹1,946,737
  • Statistician - ₹508,647
  • Business Intelligence Analyst - ₹577,745

What is meant by Data Analytics?

Data analytics is the science of monitoring raw data sets to make productive conclusions. The processes are mostly automated into algorithms and mechanical processes, which businesses use to bring efficiency into operations. We curate plans to categorise data analytics into descriptive, diagnostic, predictive, and prescriptive analyses.

Is Data Science and Data Analytics the same?

Data analytics is closely related to data science. Data Science is an umbrella term comprising data analytics, machine learning, and other related disciplines. The data analysts develop trends by analysing the existing data sets.

These innovative minds concentrate on the ‘method’ of capturing data to uncover actionable insights. Hence, the difference is that data science posts queries, whereas data analytics finds the answer.

 

Skills Needed to Become a Data Analyst

To earn exposure and experience in the field of Data Analysis, learners should know:

●     SQL

●     Statistical Programming

●     Machine Learning

●     Probability & Statistics

●     Data Management

●     Econometrics

●     Statistical Visualization

 

Career Options in Data Analytics with Average Salary

●     Data Scientist - Average salary is ₹698,413, entry-level data scientists earn ₹500,000 per annum and mid-level professionals (5-9 years of experience) earn ₹1,004,082 per annum.

●     Data Engineers - ₹ 8,56,643 per annum.

●     Data Analyst - Average salary (1 – 4 years of experience) is ₹3,96,128, mid-level analysts (5 – 9 years of experience) earn ₹6,03,120, senior level with 10-19 years of experience earn ₹9,00,000.

 

What is Machine Learning?

A branch of Artificial Intelligence, Machine Learning enables machines to automatically extract and learn from massive volumes of data and identify patterns. These advanced tech tools can devise future trends and predictions without human intervention by empowering computers to simplify human tasks: “the ability to learn.”

The technology also helps capture hidden insights based on this perceived data. A good example is Facebook, which implements machine learning algorithms to gather behavioural data from the users on this social media platform.

Based on that, the app can identify and predict posts, articles, and videos that users will find interesting to watch. Amazon and Flipkart also recommend posts and advertisements following the same technique.

 

Are Machine Learning & Data Science the same?

The expanded version that involves studying data and extracting information from them is known as Data Science. In contrast, Machine Learning is the field devoted to building reliable methods and tools that can learn the data by themselves.

These technologies use the gathered data to derive insights and predictions for overall improvement in performance. Therefore, the data scientist is typically a researcher who works with the theory behind algorithms. In contrast, an ML engineer can build models.

 

Skills Required for a Machine Learning Engineer

The tech enthusiasts must be aware of the following to become successful machine learning engineers:

●     Computer Science Fundamentals and Programming

●     Applied Mathematics

●     Data Modeling and Evaluation

●     Natural Language Processing

●     Neural Networks

●     Software Engineering and System Designs

●     Knowledge of Data Science

 

Career Options in Machine Learning with Average Salary

●     Data Scientist - ₹698,413.

●     Machine Learning Engineer - The average salary for senior machine learning engineers is ₹1,19,17,213 and can go as high as ₹1,31,81,256.

●     AI Engineer - The entry-level salary is ₹800000 LPA. Senior-level professionals earn as high as ₹50,00000 LPA.

●     Cloud Engineer - For an experience of 0-3 years, the salary is ₹12,41,000 PA. Mid-level engineers with 4-6 years of experience earn ₹17,44,817 – ₹19,00,369 PA.

 

Companies

Average Salary (USD)

Amazon

$100,481 – $302,894

Google

$720,000 – $313,797

Facebook

$185,350 – $207,430

Microsoft

$128,514 – $149,789

Apple

$119,224 – $267,481

 

Final Words

Pursuing a career in Data Science, Data Analytics, and Machine Learning is not an easy task to accomplish. Given these fields has well-paid jobs with multiple avenues for growth, doing certification courses will be an added benefit. Start today by visiting our website and brush your skills to land a successful job tomorrow.

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