Making a Successful Career in Data Science

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Making a Successful Career in Data Science
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• Data Science, Analytics, Artificial Intelligence,
  Machine Learning nowadays, we keep on hearing
  these terms quite often. There is a lot of
  glamour associated with them. Many are aspiring
  to make a career in Data Science. Why data
  science is gaining so much popularity?
• It is said that todays world is of data and
  information. We produce and consume so much data.
  For example, we use social media. We cannot
  imagine a day without a mobile phone and without
  social media- Facebook, Twitter, YouTube,
  Snapchat, WhatsApp, Pinterest, and so on. People
  are using social media for sharing photos,
  videos, documents, communicating, commenting,
  etc., Almost every business creates a website.
  Data consumption is increased tremendously after
  the exponential growth of Mobile phones. And this
  will keep on growing further, thanks to more
  advanced technologies like the Internet of
  things(IoT).

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• Every day, roughly we create 2.5 quintillion
  bytes of data. With the growing popularity of
  IoT, this data creation rate will become even
  greater. seedscientific.com
• Career in Data Science More and More Data
• Here are some interesting numbers to give you an
  idea of the data we end up generating every day,
  every minute, and every second. Every minute,
  Google does 5,700,000 searches Every minute,
  Instagram users post 65,000 photos Every
  minute, YouTube 694000 Hours streaming video
  Every minute, Zoom hosts 856 Minutes of Webinar
  Every minute, Snapchats shared 2000000 chatsAnd
  the list continues

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• That is really mind-boggling! We are aware that
  now we use so much data daily. But why we are
  discussing all this? What can be done with this
  data? Is it really useful to study this large
  data to get something interesting for business?
• And the answer is a big YES. Let us look at
  what it means by studying this data or the
  science of it.

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• Career in Data Science, understand what it is!
• When you visit search on the internet or talk to
  people, you will hear the following terms very
  often. These terms which are in fashion right now
  are data science, data analytics, machine
  learning, deep learning, big data, etc. Without
  really understanding the meanings of these terms,
  it is not possible to understand data science.

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• Let me simplify this for you from the perspective
  of the job roles or tasks. To process a very
  large volume of data to discover new insights is
  the main function of Data Science. Data
  warehousing, Data mining were the terms used for
  this kind of computing. However, in recent years
  it got more glamourized in the name of data
  science or machine learning.
• We can call Data science the big domain which
  encompasses everything. In this domain, at a very
  high level, there are two types of tasks one is
  to clean and prepare the data and the other is to
  analyze the data for insights.

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• Engineering the Data
• An important step before analyzing the data is to
  clean it and prepare for the next step. It is
  called Data Engineering. Data from various
  sources is collected, processed, cleansed,
  transformed, and stored in different ways. This
  includes everything from the file system,
  databases, data warehouses, and NO-SQL databases.
  In addition, the big data processing mechanisms
  like Map Reduce, PIG, HIVE, etc., data wrangling
  also comes in this category.
• To process data, you must have skills in
  Databases, SQL, and Programming languages like
  Python and/or a set of tools that are part of a
  big data system like Hadoop.

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• Data Analytics
• This section contains tools and technologies
  which are used to process and view data in
  different ways. Statistical analysis is done
  using different statistical tools and algorithms.
  These generate insights or patterns. Using these
  insights, businesses can take vital decisions
  like how much to order, how to arrange goods on
  the shelf of a big store, and whether to give a
  loan or not to a particular applicant? This is
  what you learn in a data science certification
  course.

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• Learning Data Science Python/R Programming
• Data engineering being more focused on data
  processing or programming may be of more interest
  to computer graduates. On the other hand, data
  analytics being statistically oriented may
  attract students from other science disciplines.
  But the bottom line is one has to have a fair
  knowledge of both to do well in a career.

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• Starting step could be learning Python and R
  Programming. Recently Python has become popular
  because of the availability of a large number of
  readymade libraries for data science algorithms.
  If you want to pursue a Data science course, make
  sure that it teaches you statistical concepts,
  Python/R Programming, and then various machine
  learning algorithms with a project.
• Why wait, take a step and enroll in a Python
  Programming course at Texceed!

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• Conclusion
• Start a Successful Career in Data Science with
  the Leading IT Training Institute in Pune
  Mumbai. Contact Now.

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• THANK YOU