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Top Essentials Skills for Data Scientists

  • July 05, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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What is Data?

Data are traits or information that are discovered by perception. They are often numerical. Technically speaking, data is a collection of qualitative or quantitative factors about a single person or item or several people or objects. A datum, on the other hand, is a single instance of a single variable.

Top Essentials Skills For Data Scientists


What is Data Science?

The following are simple definitions of Data Science:

Data Science involves many fields such as statistics, scientific methods, and data analysis, and can extract value from data.

People who use Data Science in practice are called Data Scientists. They can use a combination of different skills to analyze data collected from various sources like customers’ data, sensors, smartphones, and others.

Data Science is a discipline that uses data to learn knowledge. Its goal is to produce data products by extracting the most important parts from data. It combines theories and technologies of many fields, including Machine Learning, Data Visualization, Data Warehousing, applied mathematics, statistics, pattern recognition, and high-performance computing. Data Science uses various relevant data to help non-professionals understand problems. Data Science techniques can help us how to process data correctly and assist us in research and investigation in the fields of biology, Social Science, Anthropology, etc. In addition, Data Science is also of great help to business competition.

Data Science is related to Data Mining and Big data. It is an interdisciplinary field that extracts knowledge and insights from numerous structured and unstructured data through scientific methods, processes, algorithms, and systems.

The concept of Data Science combines statistics, Data Analysis, Machine Learning, and other related methods to facilitate the understanding and analysis of actual phenomena with the help of data. It uses technology and theories obtained from many disciplines such as Mathematics, Statistics, Information Science, and Computer Science. Turing Award winner Jim Gray envisioned Data Science as a scientific "fourth paradigm" ( empirical, theoretical research, computer-aided, now data-driven), and asserted that due to the influence of information technology and the flood of data. Everything about science is constantly changing.

Data Science one of the most exciting fields today. Why is it so important?

Because businesses are holding enormous volumes of data. Modern technology has made it possible to generate and store an increasing amount of information, and data volume has likewise rapidly expanded. 90% of the data in the world is thought to have been created in the last two years. Users of Facebook, for instance, post around 10 million images per hour. However, the majority of this data are underutilised and are often only found in databases and data lakes.

Only if we can understand the vast amounts of data that are being gathered and stored by technology can they help organisations and society everywhere. This is what data science means.

Companies may utilise data science to identify patterns and develop insights to improve decision-making and provide more creative goods and services. Most crucially, Data Science frees business analysts from having to manually explore the vast volumes of collected data to find insights that may be used to train Machine Learning (ML) models. Innovation is built on data, but only data scientists are able to get knowledge from data and then act on it to realise its worth.

Features of Data Science

Encourage reasonable switching between assertive reasoning (based on assumptions) and induced reasoning (based on patterns). The model established by Data Science no longer needs a static environment based on experience, and the model can be updated in real-time, continuously learning to improve and perfect. The deductive reasoning process is to compare the problem with the existing basic model, make related assumptions and simplifications, and use data to verify or test the rationality of the assumptions and models. Inductive reasoning is to first explore data and analyze data to discover problems, propose hypotheses or optimize hypotheses, and discover new patterns, insights, and analysis paths from the data. Data Science realizes from simple data statistics to drive decision-making, to relying on distributed data, real-time data, interactive data, etc. In general, Data Science has the characteristics of proactively discovering problems, interacting with data, advocating distributed, real-time data analysis, predicting results, executable solutions, and the need to combine multiple capabilities.

Impact of Data Science

Big data is quickly growing in importance as a tool for corporations and businesses of all kinds. Big data's accessibility and explanation have altered the business models of established businesses and aided the formation of new ones. The overall market value of data-driven businesses was 333 billion in 2015; by 2020, it will have increased to 1.2 trillion. In order to assist businesses and organisations identify best practices, data scientists are in charge of transforming huge data into actionable information and developing software and algorithms. Data science has had an equally huge influence on the globe as big data, which has continued to have a significant impact due to their tight association.

In scientific and technology circles, big data has also prompted a reexamination of scientific research techniques and is driving a revolution in scientific research methods and thinking. The study of different laws and theorems was the only focus of the early scientific investigation, which was thereafter followed by theoretical science. People started looking for simulation ways to answer real problems since theoretical analytical methods are sometimes too complex, which gave rise to computational science. A new scientific research model has been created as a result of the rise of big data. According to this model, when faced with vast data, researchers just need to directly find or mine the information, knowledge, and wisdom they need from the data, without ever personally contacting the objects to do so. In his final address, The Fourth Paradigm, Jim Grey, a 2007 Turing Award laureate, distinguished between data-intensive science and computational science by describing the "fourth paradigm" of data-intensive scientific inquiry. Grey thinks the "fourth paradigm" may be the only methodical route to addressing some of the most challenging global issues we now face. In actuality, the "fourth paradigm" represents a significant shift in how people think as well as a shift in how science is conducted.

Additionally, data analysis utilises practically all areas of contemporary mathematics. Even topics that are quite abstract, like representation theory, have a place in the field of data science. Consequently, the demands for and development of mathematics in data science are broad and not only confined to a few fields. Data should replace numbers, graphs, and equations as one of the fundamental components of mathematical inquiry.

Top Essentials Skills For Data Scientists


Who is a Data Scientist?

A Data Scientist is considered to be an engineer or expert who can use scientific methods and use different Data Mining tools to digitally produce and understand symbols, complex numbers, text, audio, video or URLs, and other information, and can find new data insights (different for statisticians or analysts). The qualities that an efficient Data Scientist needs are: understand mathematical algorithms, understand data collection, understand mathematical software, understand data analysis, understand market applications, understand decision analysis, etc.

How to Become a Data Scientist

STEP 1: Learn statistics, mathematics, and Machine Learning

The resources for learning this knowledge are given below. If you feel that these resources are too difficult to understand, you can start from the two books "Naked Statistics" and "Statistics in a Simple Way".

STEP 2: Learn to program

Master the basics of computer science and learn the whole process of system development (end-to-end development), because what you do will become part of other systems. Choose a programming language for data analysis, such as R and Python for open source software, or SAS, SPSS for commercial software, etc.

During the learning process, you can use DataCamp, tryR, Codecademy, or Google Class for interactive learning.

STEP 3: Understand the database

In most actual Data analysis projects, most of the data is stored in the database, so you learn how to operate the database, such as relational database MySQL, non-relational database MongoDB, etc.

STEP 4: Learn data preprocessing, visualization, and report production

  • Data Preprocessing

    In the work of a Data Analyst, as much as 60% of the time is spent on data preparation before actual analysis. The goal of data preprocessing is to change the data to the way we like it for later analysis and processing. It's like, how many girls like to use Meitu Xiuxiu to take pictures, and their eyes are not big, then I use Meitu Xiuxiu to make their eyes bigger. Become what you like.

    Data preprocessing can help self Coursera in "Getting and Cleaning Data" courses (Author: John Hopkins). You can also use the tools DataWrangler, R language data, table, and display packages.

  • Data Visualization is to display the results of data analysis for easy display. The utility tools are ggvis, D3, vega.

  • As the last step of data analysis, a data report is to make data analysis and results in easy-to-understand reports. Practical tools are Tableau, Spotfire, and R Markdown.

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STEP 5: Upgrade to the big data level

When you start to process massive amounts of data, most of the problems that Data Scientists have to solve cannot be done on a single machine. They need to use the distributed processing of large data sets. The tools used are Hadoop and Apache Spark.

STEP 6: Practice more and communicate more with Data Scientists

Fortunately, in the Internet age, we can get to know Daniel through the Internet and learn more experience and knowledge from the knowledge content they share. Of course, you can also gain more practical experience by participating in competitions and doing small projects.

Learn the core concepts of Data Science Course video on Youtube:

STEP 7: Internship, actual combat, or finding a job

The best way to determine if you are a real Data Scientist is to use your new knowledge to rise to the challenge and enter the field of data analysis.

STEP 8: Learn more through the professional community

Data analysis websites are DataTau, Kdnuggets, FiveThirtyEight, datascience101, r-bloggers. You can read the blogs of these big Data Science experts: Hilary Mason, David Smith, Nate Silver, DJ Patil.

Top Essentials Skills For Data Scientists


Top Ten Essentials Skills For Data Scientists

There is a growing need for additional data scientists as the demand for data science skills rises. The application of data science, however, is a separate topic that does not fall under a particular industry or business sector. The effect of a data scientist may be felt throughout an organisation.

If you pursue this road or are a quickly expanding data scientist, you are aware that education is the first step. However, certain Data Science abilities go beyond the scope of technical education. Practise and consolidation of these talents will make you stand out from the competition among scientists and job seekers as this field expands.

Data science is a field that is evolving throughout time. You can only proceed by learning the fundamentals of data science. Data science is a field that is evolving throughout time. To study more complex ideas like Deep Learning and Artificial Intelligence, you must first master the fundamentals of data science.

Data preparation and exploration, data representation and transformation, predictive analysis, data visualisation and expression, and machine learning are some of the branch areas of the vast field of data science. Knowing this may cause newcomers to become perplexed and wonder what are the fundamental competencies required of data scientists. You can learn more about the top 10 talents that data scientists need to master by reading this article.

Technical Skills of Data Scientist

In the job description of a Data Scientist, you usually see that most of the top required skills are these.

Many of these skills can be cultivated and developed in educational courses or formal business training. With the continuous advancement of analytics and data personnel, many organizations increasingly emphasize these skills.

Similar to coding, mathematics, and statistics play an essential role in Data Science. Data Scientists need to deal with mathematical and statistical models and must be able to apply and extend them. Data Scientists have a wealth of statistical knowledge and can critically think about the value of various data and the types of questions it can or cannot answer. Sometimes, problems require the design of novel solutions that can incorporate or modify existing analysis techniques and tools. Understanding the basic assumptions and algorithms is essential for using these applications.

1. The ability to prepare data for useful analysis

With this skill, you will be able to

Data preparation is the process of preparing data before analysis, including data discovery, data conversion, and data cleaning tasks. For groups such as Analysts and Data Scientists, it is a vital part of the analytical workflow. Data Scientists need to comprehend data preparation tasks and how they relate to Data Science workflows despite the tool used.

2. Ability to use self-service analysis platform

With this skill, you will be able to:

This skill is a non-technical skill because it is related to critical thinking and communication. A self-service analysis platform can help you show the results of Data Science processes and explore data. It can also help you share these results with people with lower technical levels. When creating dashboards in a self-service platform, end users can adjust parameters to ask their questions and evaluate their impact on real-time analysis as the dashboard is updated.

3. The ability to write efficient and maintainable code

With this skill, you will be able to:

This skill is almost innate. This is because Data Scientists have a good understanding of the systems designed to analyze and process data, and they must also understand the inner workings of the system. Many different languages ​​are used in Data Science. Learn and apply the language most relevant to your role, industry, and business challenges.

4. Ability to apply mathematics and statistics correctly

With this skill, you will be able to:

5. Use Machine Learning and Artificial Intelligence (AI) capabilities

With this skill, you will be able to:

In most organizations, Machine Learning or AI will not replace your role. However, using them can increase the value you deliver as a Data Scientist and help you work better and faster. A chief data officer recently shared: "To realize the promise of AI Machine Learning, you will need some typical human skills." As he said, the biggest challenge you face in the field of Artificial Intelligence is figuring out whether you have the right data. When does the "right data" show the wrong content and find "good enough" data for AI before deciding to use a beneficial and well-trained AI model?

    • Find data sources, collect, organize, process, and model data
    • Analyze large amounts of structured and unstructured data
    • Prepare and present data in the best form to make decisions and solve problems
    • Understand the advantages and challenges of using Data Visualization
    • Understand the basics of market solutions
    • Understand and apply best practices and techniques when creating analyses
    • Ability to share results through self-service dashboards or apps
    • Programs that directly process analysis, processing, and visualization of data
    • Create programs and algorithms to analyze data
    • Collect and prepare data via API
    • Perform Exploratory Data Analysis and identify significant patterns and relationships
    • Use strict statistical thinking to extract signals from noise
    • Understand the strengths and limitations of various test models and why they are suitable for a given problem
    • Understand the ways and scenarios of Machine Learning and AI applicable to business
    • Train and deploy models to implement efficient AI solutions
    • Interpret models and predictions in a way that is useful to the business

Non-Technicals Skills of Data Scientist

These abilities are the cornerstone for the rigorous application of Data Science to business challenges, but they do not need a lot of technical expertise or formal accreditation. Today, in order to succeed, even highly qualified Data Scientists must also have the following soft skills.

Yet another ability that is in high demand practically everywhere is effective communication. Whether you work as a CEO or in a junior position, being able to communicate with people is a valuable skill that may help you do your tasks quickly and efficiently.

Data scientists should be able to analyse data and communicate their results to both technical and non-technical audiences in the corporate sector. This essential component helps raise data literacy throughout the whole organisation and increases the influence that data scientists may have. Organisations will rely on data scientists to resolve problems and effectively communicate results so that others can understand how to take action when data offers solutions to a variety of problems or gives answers to business concerns.

  • Effective communication

    With this skill, you will be able to:

    • Use business-related terms to explain the meaning of data-driven insights
    • Exchange information in a way that emphasizes the value of behavior
    • Communicate the research process and hypothesis that can derive conclusions
  • Critical Thinking

    With this skill, you will be able to:

    • Analyze problems, assumptions and results objectively
    • Know which resources are essential to solve the problem
    • Look at the problem from different perspectives and angles

    Any career may benefit from having the ability to think critically. It is much more crucial for data scientists since, in addition to discovering insights, you also need to be able to define the issue correctly, comprehend how the outcomes connect to the business, and grasp how they influence the following actions.

    When dealing with data interpretation, it is also crucial to analyse the issue objectively before coming to a conclusion. In the discipline of data science, critical thinking entails the ability to analyse many viewpoints, data sources, and a constant sense of curiosity.

  • Curious

    With this skill, you will be able to:

    • Facilitate the process of searching for answers
    • In-depth research, rather than stop at the surface results and initial assumptions
    • Think creatively, eager to explore more
    • Keep asking "why" because one answer is usually not enough

    Data Scientists must be curious, eager to discover, and answer questions raised by data and answer questions that have never been asked before. The meaning of Data Science is to discover potential truths. Successful scientists will never be satisfied with "just right" but will continue to search for answers.

  • Commercial awareness

    With this skill, you will be able to:

    • Understand the business and its unique needs
    • Know what organizational problem needs to be solved and why
    • Transform data into useful results for the organization

    Data scientists have a dual responsibility: they must comprehend both their industry and the business in which they operate, in addition to knowing how to manage the data. Data Scientists should have a thorough grasp of the business to be able to handle current issues and take into account how data may assist future development and success. grasp data is just the beginning.

    Dr. NR Srinivasa Raghavan, the chief global data scientist of Infosys, stated that "Data Science is more than just digital computing: its function is to apply various skills to solve specific problems in an industry."

  • Commercial awareness

    With this skill, you will be able to:

    • Understand the business and its unique needs
    • Know what organizational problem needs to be solved and why
    • Transform data into useful results for the organization

    Data scientists have a dual responsibility; in addition to knowing their subject matter and how to interpret the data, they also need to understand the industry and area in which they operate. Data Scientists should have a thorough grasp of the business to be able to handle current issues and take into account how data may assist future development and success. grasp data is just the beginning.

    Dr. NR Srinivasa Raghavan, the chief global data scientist of Infosys, stated that "data science is more than just digital computing: its function is to apply various skills to solve specific problems in an industry."

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