Data Science is one of the biggest industry buzzwords that is commanding in 2019 and will continue to grow in the future. But to many, it is still puzzling to answer what is Data Science?

In 2019, Data Science is one of the most attractive domains to adopt and many young talented professionals are envisioning to become a part of it in real life. Thus, this data science guide is dedicated to beginners looking for a future in BI and data science as data scientists.

Through this post, you will drive inside the world of data and begin your journey of self-learning. You will also get answers to popular questions like what is data science, how data science is different from BI, and how data scientist uses data science to make predictions and accurate decisions. So, let’s spring in the world of data science.

Key Points Covered in Data Science Tutorials for Beginners

What is Data Science?

Data Science is a blend of data inference, algorithm development, and modern technology used to resolve complicated real-life business problems analytically.

In other words, data science is a detailed study that interprets data and mines it into resourceful information for formulating productive IT strategies. It is a combination of varied disciplines like mathematical statistics, data analysis and big data.

These disciplines are merged together to unwind intractable problems by recognizing patterns from structured and unstructured data and transform these puzzles into a practical solution.

Importance of Data Science in the 21st Century

Entrepreneurs are always seeking new ways that can significantly nurture their businesses. Thus, incorporating Data science into a business is a must in the 21st century, as it opens up an opportunity to add noticeable value to grow their businesses. The technology is not defined for limited industries; instead, it is effective for each and every industry functional in the market today.

Today, businesses captivate data from multiple channels like: 

  • sensors
  • financial logs (purchase transaction)
  • customer’s mobile app usage
  • Social media posts
  • Digital media (pictures, videos)

The data collected is mostly in the form of unstructured and semi-structured data; and to make the most out of this data, advanced analytical tools, algorithms, and Data Science tactics are needed.

Some major advantages of leveraging data science in business are:

  • It drives positive outcomes by using quantifiable data and analytics
  • Helps in customer targeting and retargeting
  • Assists in product/service selling and marketing as per customer’s interests
  • Mitigates risks and frauds in businesses
  • Helps in offering personalized customer experiences
  • Identifying future opportunities to grow a business
  • Helps in understanding the market and gives direction to make timely decisions at the time of market growth or degrowth

Also Read>> Data Science vs. Big Data vs. Data Analytics

7 Domains Where Data Science Applications Are Creating a Mark:

Domains Big Data Applications
Airlines Flight-delay prediction, Aircraft buying decision, Number of Halts, Designing Loyalty programs
Healthcare Quick diagnosis, applications to monitor patients, supports in pharmaceutical research
Education Course suggestions, increase student enrollment, placement help 
Journalism Data-driven journalism to track the performance of post
Agriculture Determines the right amount of seeds, fertilizers, and water, decision-based on weather forecasting predictions
Logistics Helps in improving operational efficiency, decisions to pick the best route and mode of transport  
Automotive Auto-driving cars

Data Science Life Cycle for a Project

After looking at the real-life applications, let us understand what is the data science process that a data scientist uses in accomplishing a project.

Data Science Life Cycle

Data Science lifecycle

To successfully walk through this data science process, data analyst always consider some of the key components of data science like:

  • Data

Data is the foundation of Data Science, and to get accurate results, data plays an essential role. To solve any business problem, Analysts must collect two types of data: structured data and unstructured data. Structured data is mostly in the format of SQL tables, RDBMS, OLTP; and this form of data is mapped with pre-designed fields. Whereas, unstructured data is in the form of digital images, videos, emails, tweets, sensor data, word files, and PDF files.

  • Programming Language

To manage data and undergo a smooth analysis, two languages,Python and R, are popularly used. These programming languages are robust and extremely effective to find meaningful conclusions.

  • Statistics & Probability:

Statistics and probability are the two pillars of data science, and thus, every data scientist professional must have a clear understanding of these mathematical foundations. Someone having a broad exposure to these foundations will ensure that data is not misinterpreted and thus save from making wrong conclusions.

  • Machine Learning

Knowledge of machine learning algorithms is essential for a data scientist as you have to daily use algorithms like regression and classifications to find optimum solutions. These algorithms help in predicting valuable and unseen insights from the available data.

  • Big Data

Data science has its roots from big data, and hence, both of them are inseparable. Big data is a diverse form of data generated from multiple sources, and to process this data, data scientist uses scientific programming tools like Java, Hadoop, and Apache Spark.

Also Read>>How are Data Scientist and Data Analyst different?

Difference Between BI and Data Science 

Data science, a data-driven approach, is used for analyzing future predictions for any business. Whereas business intelligence (BI), helps in assessing the past performance of a business by referring to the current state of business data.

When data science was not in the picture, BI was very much in the trend. But as the business complexities evolved, data science is a more practical approach used by businesses to find solutions.

So the common difference between BI and Data science is big data helps in answering depending upon the past, what should a business change in the present!But, data science answers the question – why it happened in the present and based on this, what can happen in the future.

So today, businesses use both BI and Data Science to resolve complex problems by breaking the raw data into meaningful information.

Difference Data Science Business Intelligence
Complexity Highly complexed Simple
Usage Foresee upcoming situations Analyze the root cause of past failure or success
Tools Apache Spark, SAS, BigML  Pentaho, Sisense, Birst

How is Data Science Different from Machine Learning?

After looking at the difference between BI and Data Science it’s time to see how data science and machine learning are not the same.

Have you observed while surfing on Facebook, you find articles, posts, and videos that exactly match your interests. If yes, you have seen a live example of machine learning because leading names like Facebook, Amazon, and Netflix are using machine learning algorithms to promote what their users actually want to see by tracking users’ past behaviors.

Data science is a broad spectrum, and machine learning is a component of data science. It uses machine learning aspects for smooth functionality. So via machine learning technology and complex algorithms like regression & supervised learning; one can learn and process data without the help of human interventions.

Leading Data Science Tools Used in 2019

Data Science involves roles like data acquisition, data entry, data mining, clustering, data modeling, data warehousing, data processing, predictive analysis, regression, data reporting, and decision making. For an effective flow and achievement of these functions, data scientists use modern data science tools.

Some of the frequently used data science tools are:

Data Science Tools Free/Paid Features
RapidMiner Free-Trial of 30 days Data preparation, machine learning, model deployment, central repositories, big-data analytics, cloud-based repositories
Apache Hadoop Free An open-source framework, large data processing, failure detection
Apache Spark Free Supports data-processing solution, working on structured data using SQL module, uses DataFrame API
Data Robot Paid Supports parallel processing, has Python SDK and APIs, model optimization

A career in Data Science

The data science tutorial for beginners helps you in understanding the path that leads you to become a data science professional.

What is the typical KRA of a Data Scientist?

Data scientists are the professionals who can manage a colossal amount of data and formulate it to dig-up meaningful solutions out of this data. For this, a prospect should learn to develop, construct, test, and maintain a large chunk of the database.  Further, a professional data scientist must equip himself or herself with prominent data science tools, techniques, methodologies, and algorithms.

Also Read>>Skills That Employers Look For In a Data Scientist

Data Science Job Titles Trending in the Markets

  • Data Scientist
  • Data Engineer
  • Data Analyst
  • Business Intelligence Analyst
  • Business Analyst

Prerequisite Skills to get a job in Data Science 

To start a career in data science, one must have a basic understanding of:

  • Data analysis
  • Machine learning
  • Statistics and Probability
  • Hadoop (preferred)

What is the Salary of Data Scientist?

Good Data Science professionals are in high demand today, and hence, these experts fetch handsome salaries. The average salary of data scientists having 2-3 years of experience is around 8-10 Lakhs.

Best Data Science Courses to Kick-Start Your Career in Data Science

If you are looking for a career in data science, it is highly recommended for you to take up data science certification courses, which will outshine your skills and add a benchmark on your resume.

Some of the Top Data Science Courses are:

Course Duration & modules Mode of Learning Know More
Certified Data Scientist Expert 60 hours of eLearning content with 167 modules Online Self-Study Know More
Data Science Online Training Course 40 hours of instructor-led sessions with 47 modules Online Classroom Know More
Data Science with R Professional 4 hours of eLearning content with 66 modules Online Self-Study Know More
Data Scientist Master’s Program  74 hours of instructor-led sessions with 76 modules Online Classroom Know More

Wrapping Up

The future is data and someone having a stronghold in data will be always in demand. Data has changed the way in which businesses deal with problems. So if you too are planning to join the marathon of Data Science, it’s time to gear-up and take leading data science certification courses to uplift in your career.

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