Top Skills That You Should Master to Become an Awesome Data Scientist - PowerPoint PPT Presentation

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


As the demand for Data Scientists rises, the field becomes more appealing to students and working professionals. Thanks to big data’s role as an additional perspective engine, Data Scientists are in high demand at the organizational level across all vertical markets. – PowerPoint PPT presentation

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

Top Skills That You Should Master to Become an
Awesome Data Scientist
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.
  • 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//
Technical Skills Required to Become Data
Scientist Following are some of the essential
technical Data Scientist skills
  • 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
  • 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.
  • 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.
  • 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.
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.
  • 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
  • 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.
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
  • 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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