With the emergence of data science, the present business domain has become logical like never before. They now correlated occurrences and events rationally to identify the cause of problems and come up with possible solutions. As a massive amount of data is being generated by organizations, the search for skilled data scientist<\/strong>s has increased exponentially.<\/p>\n

Undeniably, a career as a data scientist<\/strong> is highly lucrative and rewarding, but you need to stay on top of the game to enjoy those results. Data scientists<\/strong> are often considered a rare breed by many because of the mix of skills they possess. In order to make sense of the massive amount of data captured on a regular basis and utilize it to solve critical business problems, identify trends, and make decisions that can support new ideas, businesses need professionals with a mix of coding, databases, data visualization, statistics, data\u00a0 preparation skills, and machine learning.<\/p>\n

To become an effective data scientist<\/strong>, you need to have robust data skills<\/strong> along with a great practical skillset. You probably have already heard what data science experts often say: 80 percent of a data scientist\u2019s<\/strong> job involves data cleaning. You should understand that data science isn\u2019t all about doing predictive analytics and machine learning 24\/7. Instead, you\u2019d need to employ your data skills<\/strong> first to complete different steps before you can run a proper machine learning algorithm. These steps usually include data collection, data formatting, data cleaning, transforming the data to a proper format, discovering as well as understanding the data, running various data analytics projects, data visualization, and more. In other words, having polished data skills<\/strong> is an extremely important aspect of any successful data scientist<\/strong>.<\/p>\n

In this article, we\u2019re going to look at different ways in which a data scientist<\/strong> can polish his\/her data skills<\/strong>.<\/p>\n

Key tips to improve data skills<\/em><\/strong><\/h3>\n

Having a clear understanding of the business case<\/em><\/h4>\n

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When a data scientist<\/strong> is presented with a problem, in most of the cases, the initial time is spent on finalizing and identifying the means to attain the ultimate goal instead of focusing on the goal itself. Without a clear understanding of the business case, the probabilities for a data scientist<\/strong> to come up with a solution that doesn\u2019t meet the client expectation is higher. Hence, there\u2019s a great need for data scientists<\/strong> to clearly understand both the business case and client expectations before they decide a course of action.<\/p>\n

Charting the hypothesis<\/em><\/h4>\n

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There\u2019re always probable outcomes to every probable question and that\u2019s something every data scientist<\/strong> needs to consider. Hence, it\u2019s very crucial for every data scientist<\/strong> to understand possible loopholes, chart the possible outcomes, and develop a solution in accordance with those.<\/p>\n

Observing the trends<\/em><\/h4>\n

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Having a clear understanding of the particular industry where a data scientist<\/strong> is working and following the recent trends can help him\/her to identify the business drivers. Hence, every data scientist<\/strong> should make it a frequent practice to follow internal trends in his\/her day-to-day work. Understanding the unique perspective and information provided by the functions and defining their impact on the business level strategic thinking are one of the key steps to improve data skills<\/strong>.<\/p>\n

Here\u2019re the key areas on which every data scientist<\/strong> should focus on to polish his\/her overall data skills<\/strong>.<\/p>\n

Numeracy skills<\/em><\/h4>\n

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These involve mathematical skills that include an array of abilities like having a good understanding of numbers and figures, understanding the existing relationships between numbers, interpreting any mathematical information, having the ability to organize information, knowing how to measure as well as analyze data, having calculation skills etc.<\/p>\n

Analytical skills<\/em><\/h4>\n

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These skills refer to a data scientist\u2019s<\/strong> ability to capture, view as well as analyze all sorts of information in details. They also refer to the ability to view a situation or challenge from different perspectives. Analytical skills are crucial data skills<\/strong> which make it possible for a data scientist<\/strong> to address business problems by forming decisions in the most useful way. Hence, to become a successful data scientist<\/strong>, you\u2019ve to acquire and polish your analytical thinking and skills.<\/p>\n

Attention to details<\/em><\/h4>\n

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This is one of the crucial data skills<\/strong> that any data scientist<\/strong> needs to develop. Ability to pay complete attention to details enables a data scientist<\/strong> to identify initially unseen links and details. This ability is particularly important when it comes to solving problems and making decisions. Also, one with this ability tends to perform better and stands a lower possibility of making errors.<\/p>\n

Here\u2019re the subjects that every data scientist<\/strong> should try to master to boost his\/her data skills<\/strong>.<\/p>\n

Data mining<\/em><\/h4>\n

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Data mining, which refers to the process of analyzing massive datasets to develop insights and identify patterns, is experiencing high demand as more companies and industries seek to make sense of the captured data and predict efficient outcomes. This is one of those data skills<\/strong> that not only help big businesses but every company where correlation and patterns matter.<\/p>\n

Natural language processing<\/em><\/h4>\n

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Natural language processing has the potential to transform any business that depends heavily on human interaction. Teaching a machine to understand the complexities of human language is a highly difficult process that needs specialized skills. Data scientists<\/strong> with a robust analytical background should focus on this field to remain in-demand.<\/p>\n

Machine learning<\/em><\/h4>\n

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The subfield of AI involves computer systems utilizing algorithms and data to teach themselves to come up with predictions without being programmed. Machine learning is being heavily used in advanced technologies like personalizing the consumer experience, self-driving cars, among others. The field is a great combination of data skills<\/strong>, software engineering, mathematics etc and thus it requires an array of skillsets to become an expert. Data scientists<\/strong> looking to improve their skills in this field can join communities of machine learning engineers and data scientists<\/strong> where members work together to build models, publish datasets, and compete to solve different data science problems.<\/p>\n

Python<\/em><\/h4>\n

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Python has become one of the fastest-growing and widely used programming languages in recent years. It\u2019s also a powerful data visualization tool that comes with a set of libraries which include some specific to machine learning like SciPy, NumPy, Pandas, and scikit-learn. Data scientists<\/strong> can improve their Python skills either by learning on their own with the help of online tutorials or by joining a coding bootcamp.<\/p>\n

R<\/em><\/h4>\n

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This open-source statistical software package helps data scientists<\/strong> to simplify the analysis of massive datasets and come with features like clustering, linear and non-linear modeling, time-series analysis etc. R is gaining steady popularity along with Python and is one of the most crucial skills in the data science domain. R also enables data scientists<\/strong> to perform predictive and statistical analysis on real-time data, and develop visuals to communicate the findings to the business side. Again, data scientists<\/strong> can either join a coding bootcamp or take the self-learning route.<\/p>\n

Hadoop<\/em><\/h4>\n

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This software framework helps to store and process huge volumes of data across different clusters of computing devices. It\u2019s flexible, scalable, and helps businesses to identify trends and predict results to better decision-making. While a data scientist<\/strong> can get a job with limited Hadoop knowledge, a robust knowledge of the framework is a solid selling point which may lead to more opportunities.<\/p>\n

SQL<\/em><\/h4>\n

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SQL or Structured Query Language is a domain-specific programming language which helps data scientists<\/strong> to retrieve data and gives them a way to access as well as manipulate huge amounts of information found in relational database management systems. SQL commands have the ability to capture and break down data, and edit database indexes and tables to improve accuracy. There\u2019re some interactive tools available on the internet that lets programmers test and share queries.<\/p>\n

Data visualization<\/em><\/h4>\n

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Businesses and organizations are generating a huge amount of data every day. Data scientists<\/strong> have to be able to translate this data into a format which is simple to understand in order to convert the data. Graphical representation and pictorial formats make it much easier for common people to understand the findings. Data visualization tools like Tableau, ggplot2, RapidMiner etc are used to ease this task. Hence, data scientists<\/strong> need to focus on improving this skill.<\/p>\n

Statistics<\/em><\/h4>\n

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Proficiency in mathematics and calculations is a fundamental need for any data scientist<\/strong> to be able to perform tasks that involve crucial data skills<\/strong>. Hence, it\u2019s essential to have a solid understanding of statistics as well as statistical analysis. In case one fails to have adequate knowledge of the core statistical concepts, it can become highly difficult to understand how statistical modeling works. To improve statistical skills, data scientists<\/strong> can start with basic statistics, inferential, and descriptive statistics.<\/p>\n

Final Takeaway<\/em><\/strong><\/h3>\n

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When you\u2019re trying to become a successful data scientist<\/strong>, you shouldn\u2019t only focus on improving your machine learning, deep learning or any other specialized skills. Instead, you should work on polishing the above areas and skills to improve your overall data skills<\/strong>. Being a master of machine learning or deep learning surely sounds exciting, but if you\u2019ve just entered the field of data science, you should focus on polishing your data skills<\/strong> which are crucial for advancing to the next level.<\/p>\n