Top Skills That You Should Master to Become an Awesome Data Scientist

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Top Skills That You Should Master to Become an
Awesome Data Scientist
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As the demand for Data Scientists rises, the
field becomes more appealing to students and
working professionals. Thanks to big datas role
as an additional perspective engine, Data
Scientists are in high demand at the
organizational level across all vertical markets.
Organizations are constantly relying on Data
Scientist skills to keep a step ahead of the
competition, even if it is to improve customer
retention, refine product development, or mine
data for new business opportunities.
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• So in this article, we will discuss the top
  skills required for Data Scientists
• Skills Required to Become a Data Scientist
• Lets look at two different types of skills
  needed to become a Data Scientist
• Technical Skills
• Non-technical Skills
• Let us now look at the technical skills required
  for the role of a Data Scientist.
• https//youtu.be/pD8YM2kea4M

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Technical Skills Required to Become Data
Scientist Following are some of the essential
technical Data Scientist skills




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• 1. Programming Languages (Python or R)
• To be a Data Scientist, you need to know
  programming languages such as Python, R, Java,
  Perl, C/C, and SQL. Python and R are the most
  common coding languages required for data science
  roles. Data Scientists can use these programming
  languages to organize unstructured data sets.
• Python As your knowledge of Python fundamentals
  grows, youll want to look into Python libraries,
  which are replaceable pieces of code that you can
  use instead of rewriting basic instructions.
• R R is an open-source statistical programming
  language with tools for presenting and
  interacting data-driven outcomes.
• SAS SAS is a software package that includes
  installed statistical functions and a Graphical
  User Interface (GUI) to assist non-technical
  users.

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• 2. Machine Learning
• The process of writing code that allows a
  computer to learn from initially stored data is
  known as machine learning. Machine learning is
  helpful for data scientists because it allows
  them to make essential estimations for wise
  decisions in an authentic way without the need
  for human involvement.
• 3. Data Visualization
• The process of interacting and converting data
  and information in a visual context, typically
  using a graph, chart, bar, or another visual aid,
  is known as data visualization. In visualization,
  images are also used to converse the
  relationships between various data sets.
• Power BI Power BI is available in desktop,
  mobile, and cloud versions, and it generates a
  variety of visualization techniques using Azure,
  SQL, and Excel. Beginners will find it simple to
  pick up.
• Tableau Tableau is a more sophisticated tool
  with increased speed and functionality. Users can
  create reports (heat maps, line charts, scatter
  plots, and so on) and stunning dashboards using
  drag-and-drop functions.

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• 4. Mathematics
• Mathematics is essential for data science because
  mathematical concepts aid in identifying patterns
  and the development of algorithms. Putting such
  algorithms into practice in data science
  necessitates a thorough understanding of multiple
  statistics and probability theory concepts like
• Linear Algebra Linear algebra is the fundamental
  basis of many popular algorithms, and identifying
  matrices and vectors will be highly beneficial,
  particularly if you excel in machine learning.
• Multivariate Calculus Refresh your knowledge of
  mean value theorems, gradients, derivatives,
  limits, the product and chain rules, Taylor
  series, and beta and gamma functional areas.

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• 5. Data Wrangling
• After gathering data from various sources, youll
  almost certainly come across some sloppy data
  that needs to be overhauled. Data wrangling is
  based on coding languages and assists in the
  correction of data flaws such as incomplete data,
  chain formatting, and date formatting. It is also
  necessary to map data fields from the source to
  the destination.
• 6. Statistics
• Statistics is a collection of mathematical
  methods and tools that allow us to answer
  important data questions. Every company is
  attempting to become data-driven. This is why
  there is such a high demand for Data Scientists
  and Analysts. We must now make sense of the data
  to solve problems, answer questions, and map out
  a strategy. Fortunately, statistics provides a
  set of tools for obtaining those insights.
  Sub-fields of statistics are
• Probability As a Data Scientist, youll need to
  know about Bayes theorem, probability
  distribution functions, the Central Limit
  Theorem, expected values, standard errors, random
  variables, and independence.
• Statistical Analysis and Computing It is the
  theory and practice of collecting, analyzing, and
  presenting large amounts of data to uncover
  hidden patterns and trends. Statistics are used
  on a daily basis to create more scientific
  decisions in research, industry, and government.

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7. Deep Learning It is a branch of computer
science based on computer algorithms that learn
and improve independently. Deep learning utilizes
deep neural networks to simulate how humans think
and learn instead of machine learning, which uses
more straightforward concepts. 8. Processing
Large Data Set Large data processing is a
collection of techniques or frameworks for
accessing large amounts of data to obtain helpful
information for decision support and
assistance. 9. Big Data Big data is regarded as
a high quantity, high velocity, or diverse set of
data resources that necessitate unique forms of
processing to enable better decision making,
insight discovery, and process optimization.
There has been a massive increase in data due to
apps and social media development and growth, and
people and businesses are moving online. Simply
looking at social media platforms, we can see
that they interact and attract over a million
users every day, allowing data to grow faster
than ever before. So a deep understanding of big
data is essential for data science.




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• 10. Knowledge of SAS and other Analytical Tools
• Acknowledging analytical tools is a vital Data
  Scientist skill for discovering valuable
  information from a well-organized dataset. SAS,
  Hadoop, Spark, Hive, Pig, and R are the most
  popular data analytics tools used by Data
  Scientists. Certifications like SAS data science
  certificate can help you gain this valuable data
  scientist skill by establishing your expertise in
  these analytical tools.
• SQL In relational database management systems,
  SQL allows you to store, query, and manipulate
  data.
• Spark Spark is a handling source that can
  operate with massive, unstructured datasets and
  easily integrate with Hadoop.
• Hadoop Hadoop is an Apache Software Foundation
  open-source software library for distributing big
  data processing over a group of computing devices.

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11. Adept at Working with Unstructured Data Data
Scientists must have similar responsibilities
with unstructured data from various sources. For
example, a Data Scientist who is working on a
project to assist the marketing team with
providing insightful data analysis should also be
familiar with social media. Non-technical skills
Required to Become Data Scientist Well now
focus on non-technical skills that are required
to become a data scientist, in addition to
technical Data Scientist skills. It includes




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• 1.Strong Business Acumen Strong business acumen
  is the most effective way to channel technical
  skills. Without it, an aspiring Data Scientist
  may be unable to identify the issues and
  challenges that must be addressed for a company
  to grow.
• 2. Strong Communication Skills Communication is
  the next most important Data Scientist skill.
  Data scientists are experts at extracting,
  interpreting, and analyzing data. However, for
  you to be successful in your role and for your
  organization to benefit from your services, you
  must be able to communicate your findings
  effectively with team members who do not share
  your professional background.
• 3. Great Data Intuition One of the essential
  non-technical Data Scientist skills is great data
  intuition. In large data sets, valuable data
  insights are not always obvious, and a skilled
  Data Scientist has intuition and knows when to
  look beyond the ground for helpful information.

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