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    Adyar,Chennai-20,
    Velachery,Chennai-42.

Data Science Course

What is data science?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

Data science is a data collection process, followed by data storage, segregation, and data analysis to retrieve value from enormous unstructured data, helping businesses and Organisations to make data-driven decisions.

What’s data analytics?

            Data analytics is just one part of data science. It involves the process of collecting, analyzing, and interpreting data. A data analyst should know how to understand data patterns and have the basic knowledge of statistics and data models. A lot of data visualization happens in a typical data analysis process.

Data wrangling is also one part of data analysis, wherein an analyst needs to clean and segregate data based on its complexity levels. Data wrangling is a time-consuming process and one of the trickiest parts of a data analyst’s job. 

Difference between data science and data analytics

            Data science is therefore a multidisciplinary field, which involves data engineering, machine learning, data analysis, data mining, and data visualization. This means the process of collecting, cleaning, analysing and transforming large chunks of data is called data science.

As discussed above, data analytics is just one part of data science. It is thus a niche area that involves the collection and analysis of data based on complexity levels. It focuses more on analysing raw data to draw insights using different algorithms and techniques.

Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application. Python provide great functionality to deal with mathematics, statistics and scientific function. It provides great libraries to deals with data science application.

One of the main reasons why Python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background. It is also more suited for quick prototyping.

According to engineers coming from academia and industry, deep learning frameworks available with Python APIs, in addition to the scientific packages have made Python incredibly productive and versatile. There has been a lot of evolution in deep learning Python frameworks and it’s rapidly upgrading.

What is the importance of data science?

The principal purpose of Data Science is to find patterns within data. It uses various statistical techniques to analyze and draw insights from the data. From data extraction, wrangling and pre-processing, a Data Scientist must scrutinize the data thoroughly.

Advantages of Data Science :-

          In today’s world, data is being generated at an alarming rate. Every second, lots of data is generated; be it from the users of Facebook or any other social networking site, or from the calls that one makes, or the data which is being generated from different organizations. And because of this huge amount of data the value of the field of Data Science has a number of advantages. Some of the advantages are mentioned below :-

  • Multiple Job Options

     Being in demand, it has given rise to a large number of career opportunities in its various fields. Some of them are Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

  • Business benefits

     Data Science helps organizations knowing how and when their products sell best and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

  • Highly Paid jobs & career opportunities

     As Data Scientist continues being the sexiest job and the salaries for this position are also grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year.

  • Hiring benefits

    It has made it comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment teams.

  • Delivering relevant products

   One of the advantages of data science is that organizations can find when and where their products sell best. This can help deliver the right products at the right time—and can help companies develop new products to meet their customers’ needs.

  • Personalized customer experiences

   One of the most buzzworthy benefits of data science is the ability for sales and marketing teams to understand their audience on a very granular level. With this knowledge, an organization can create the best possible customer experiences.

  • Healthcare: Data science can identify and predict disease, and personalize healthcare recommendations.
  • Transportation: Data science can optimize shipping routes in real-time.
  • Sports: Data science can accurately evaluate athletes’ performance.
  • Government: Data science can prevent tax evasion and predict incarceration rates.
  • E-commerce: Data science can automate digital ad placement.
  • Gaming: Data science can improve online gaming experiences.
  • Social media: Data science can create algorithms to pinpoint compatible partners.

 DATA SCIENCE JOB ROLES

  1. Data Scientist
  2. Data Analyst
  3. Data Engineer
  4. Data Architect
  5. Data Storyteller
  6. Machine Learning Scientist
  7. Machine Learning Engineer
  8. Business Intelligence Developer
  9. Database Administrator
  10. Technology Specialized Roles

The scope and future of data science in India

Implementation of data science and analytics, machine learning, and artificial intelligence have made data management highly convenient, with most of them making their way to India. Data science is expected to surge across various sectors with improved opportunities. With so many industries involved and seeking to incorporate it, data science demand in future is inevitable.

Career in data science in India

You ask, Is data science a good career in India?

Currently, data science and artificial intelligence have slowly made their way into sectors like travel, healthcare, education, stock market, and e-commerce.

In India, if you have  a job experience of working as a data scientist, you can move onto other roles like

  1. Data architect
  2. Data engineer
  3. Data analyst
  4. Business intelligence analyst
  5. Database administrator

Top recruiters that hire data scientists

The companies where you can work as data scientists are –

  • Google
  • Apple
  • IBM
  • Amazon
  • Accenture
  • JP Morgan Chase
  • Microsoft

Who can be a data scientist?

There are two categories of people who can become a data scientist –

  1. IT students and professionals – these categories of students have either studied computer science or an IT course and have a bachelors or masters in a related field. Similarly, IT professionals who want to grow professionally and move up the career ladder take up data science courses to upgrade themselves.
  2. Non-IT students and professionals – these categories of students are from completely different backgrounds and they have interest in working in fields like artificial intelligence, machine learning, big data, and data analysis. These people tend to choose data science courses because they want to switch careers for both personal and professional reasons.

What are the skills needed to be a data scientist?

There are a few qualities that every person willing to be a data scientist must possess. These are –

  • Statistical and logical thinking attitude
  • Technical expertise

If you want to be known as a data scientist, you must possess technical expertise in the following areas –

  • Statistics
  • Deep learning
  • Big data frameworks
  • Exploratory data analysis [EDA]
  • Knowledge of data exploration, data processing, data transformation, and data loading.
  • Knowledge of Python and other programming languages.
  • Patience and interest
  • Creativity and curiosity
  • Communication skills