Top Skills That You Should Master to Become an Awesome Data Scientist
                     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 
data’s 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
Let’s 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.
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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, you’ll 
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, you’ll 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, you’ll 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
We’ll 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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Data Science with InfosecTrain
Data Scientists are known as “professionals with a wide range of skills that are 
rarely found in a single person.” This explains why Data Scientists are in such high 
demand, and why becoming one can be difficult. However, proper training and 
certification for Data Scientists are commonly the essential foundation for 
excellence. Enroll in InfosecTrain’s Data Science Program today to take the first 
step toward your career goals.
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