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1. Making a Successful Career in Data
Science

2. 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 today’s 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).

3. 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 chats
And the list continues…

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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!

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

12. THANK YOU