Essential Data Scientist Skills

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Jan 09, 2024

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According to Forbes - We conduct 40,000 search queries per second (on Google alone), which amounts to approximately 3.5 searches daily and 1.2 trillion annually. 

No one enjoys dealing with vast amounts of data unless they're a data scientist, and to become one, one needs a specific set of skills, including both technical and non-technical ones. To become one, however, it requires education. We will explore the key skills every aspiring and established Data Scientist requires to succeed at their profession. This blog covers everything from data enthusiasts looking to begin this exciting journey to experienced professionals looking to hone their craft!

Top Must-have Skills for Data Scientists

Skill sets for data scientists focus on practical abilities and soft business skills. Here are the top data scientist skills you need to have in 2024 which are crucial for any data science professional to be successful in their profession:

1. Linear Algebra

Linear Algebra is an essential mathematical subject to data science and machine learning, making up one of the three cornerstones of machine learning: machine learning models may often include matrix representations, while datasets often resemble matrix-shaped representations.

2. Statistics

At the core of data science is statistical analysis and its application, using it to uncover patterns within data to create actionable evidence. Statistics allow data scientists to collect, assess, analyze, derive conclusions from, and apply quantifiable mathematical models to relevant variables.

3. Microsoft Excel

A good Excel spreadsheet makes organizing unstructured data into an understandable form easier, making it simpler to glean insights that can be applied. Excel allows you to modify fields and functions that perform computations for you when dealing with more complex information, while users are also able to identify and construct ranges; filter, sort, merge, clean, and trim data as needed; generate pivot tables/charts as well as utilize Visual Basic for Applications (VBA).

4. Decision-Making

To leverage data science effectively for improving decision-making, data scientists must first gain an understanding of how decisions impact outcomes and integrate common machine learning technologies with knowledge of causal links that exist within data.

5. Fundamentals of Data Science

To become proficient in data science, one must understand its core fundamentals, such as machine learning and artificial intelligence and know the differences between Deep Learning and Machine Learning. Additionally, data scientists should understand their respective advantages.

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Technical Data Science Skills Required

Below is a breakdown of some of the essential technical skills required for data science, broken into three subcategories - statistical skills, mathematical skills, and programming abilities needed by Data Scientists.

Statistical and Probability Skills

Thinking in statistics is central to data science; you cannot become a Data Scientist without possessing this expertise. Data Science is simply another name for statistics!

While traditionally, only those with degrees in Statistics could study Data Science, nowadays, anyone can do it. There are numerous books that offer statistical insights about data Science as well as teaching practical aspects of it.

As part of your data science journey, we will introduce some key statistical concepts. Statistics are generally divided into two categories - descriptive statistics and inferential statistics.

  • Descriptive Statistics 

Descriptive Statistics is concerned with summarizing and describing data. It quantifies large features of data through visualizations while outlining samples from larger populations of values. Some measurements used in descriptive statistics include normal distribution, variability, kurtosis & skewness and central tendency.

  • Inferential Statistics

Inferential statistics is about drawing inferences or inferences from data, concluding smaller samples to generalize over a wider population. Various statistical methods are involved with inferential statistics, which must be familiar to you to succeed in this process. Some methods include Central Limit Theorem, Hypothesis Testing, ANOVA and Quantitative Data Analysis - essential techniques that will equip you with all the statistics skills.

An essential skill needed to become a data scientist is Probability. Probability concepts form the backbone of data science and must be grasped to carry out complex machine-learning operations effectively. They're used for inferential statistics as well as creating Bayesian Networks.

As machine learning algorithms like Naive Bayes rely heavily on conditional probability, you must also become adept in its usage. Conditional probability allows you to determine the uncertainty and risks surrounding events, enabling more informed decisions that benefit your company.

Stats and Probability both play an essential part in being a Data Scientist.

Ability to Handle Massive Amounts of Data

Data production has seen exponential growth since 2009, most of which falls into the unstructured category.

Unstructured data refers to information that does not reside in a traditional row-column database; examples include videos, photos and audio messages. As data scientists seek meaning from this massive amount of information regardless of its form, dealing with structured and unstructured forms should be fine.

Data Visualization

As companies generate more and more data, it must be presented in an easily understandable format in order to make decisions effectively. As a data scientist, one must be adept at Data visualizing that data using tools like Tableau, Plotly, Visual.ly, D3.js and Power BI; also crucial is knowing the principles behind visually organizing it together - one of their key roles as organizations directly engage with data via visualization.

Programming Skills

Excel might have sufficed 20 years ago when dealing with structured and unstructured data generation. Still, due to today's vast volume of structured and unstructured information originating, data scientists should possess programming tools such as Python and  R. They offer more flexibility for training the data set with various statistical techniques and improving analysis process efficiency.

Data Manipulation

Too often, the data we need to be more organized for data scientists to work with effectively. After extracting it from data lakes, the first step should be addressing any imperfections present - missing values, irregular strings such as LA for Los Angeles and date formats that change, such as 10/09/2009 to 2009/09/10 must all be eliminated before commencing training or analysis.

Data Wrangling

Sometimes Data Scientists must deal with incomplete or messy data sets that are initially incomprehensible or useless for further processing. In such a case, Data Wrangling involves cleaning and reformatting this unusable information into an accessible format to make sense to users and be understood.

Data Wrangling

Data Wrangling refers to the practice of collecting, cleaning and transforming raw data into another format in order to make analysis meaningful. Data Wrangling may also be known as Data Munging or Cleaning. Examples of data wrangling include:

  • Merging multiple data sources into one dataset for further analysis.
  • Removing information that is irrelevant to the analysis.
  • Filling any gaps by managing NULL values or outliers.

Knowledge of Cloud 

As Big Data continues to evolve, cloud services have quickly become the solution for companies aiming to optimize their data systems.

Amazon Web Services, Microsoft Azure and Google Cloud are among the leaders of cloud computing technology today, offering tailored services tailored specifically to their client's needs. Mastering cloud computing technology could open doors to lucrative careers in data science.

Also Read: What is Data Wrangling?

Non-Technical Data Science Skills Required

This section will move beyond technical Data Scientist skills to examine various interpersonal/soft skills for data science. Too often, data scientists focus solely on learning technical abilities while neglecting developing key soft skills necessary for their success as Data Scientists. We will examine several key interpersonal abilities required of Data Scientists, such as empathy.

Communication Skills For Data Scientists

Communicating effectively is of utmost importance, especially as one of the non-technical skills you must pay attention to in Data Science. Key areas where communication plays an integral part include Data Visualization and Storytelling.

Teamwork

Teamwork is another essential attribute of Data Scientists. Working on projects which require multiple team members' contribution is paramount. As a Data Scientist, you must collaborate with multiple company members, including business analysts, to understand customer requirements, the marketing department, and software teams for product development. Therefore, teamwork is crucial.

Work closely with your team members to identify business problems, analyze data to resolve them using analytical solutions, meet deadlines and deliver products on schedule.

MLOps (Multi-Layer Ops Management)

Data scientists and operations specialists may collaborate efficiently using MLOps, an assortment of techniques. By applying these procedures to large production systems, automated deployment of Machine Learning/Deep Learning models may be achieved while simultaneously increasing quality standards while streamlining management procedures.

Intellectual Curiosity

Analyzing an organization's data often does not yield direct answers or visible results, yet the more questions you ask, the more answers will emerge from within its pages. Curiosity can be defined as the desire to understand something better, making intellectual curiosity an essential trait in data scientists.

Strong Business Acumen

Without understanding an organization's data or elements in their business model, all the technical skills that a data scientist possesses won't produce the desired results for an organization without being able to prioritize features present in datasets as necessary and which features should be prioritized last. A data scientist who understands an organization's business model and data will help address potential sustainability and expansion obstacles.

Data Intuition

Data Intuition may be one of the most essential skills for successful data scientists. Generating insightful analyses from data is no simple task. Data Scientists need a good sense of where and when to search for important information within it - otherwise, valuable insights might never surface! This soft skill makes them efficient in their work and is invaluable when creating valuable insights from large amounts of data sets. Even if this intuitive sense does not come naturally to you, don't despair; experience and hands-on practice can help develop it further over time with different data science projects!

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Conclusion

Technology's rapid progress has created numerous potential job openings in the tech industry. As businesses and other organizations wrestle with vast data pools containing billions of data points, the need for qualified Data Scientists has skyrocketed. No matter your level of data expertise or desire, developing the essential skills for Data Scientists through a data science course is absolutely crucial to harnessing its full potential and ensuring you create plans and strategies to succeed in today's data-driven landscape.

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