Top 16 Must Have Technical Skills For Data Scientists

Top 16 Must Have Technical Skills For Data Scientists

Data science is a broad term that involves artificial intelligence and machine learning. It also involves data analytics, data mining, deep learning, and other related fields.

It is clearly one of the fastest growing fields both in terms of salaries for professionals and career possibilities. Data jobs are expected to have the highest demand and growth in the next decade. This is according to a 2020 report of the World Economic Forum’s future of work.

The same report states that a lot of workers will need retraining in their core skills. They will need to reskill and up-skill or risk being displaced by the high number of candidates seeking big data jobs via job sites. These sites are the most comprehensive sources of data and analytics experts seeking job opportunities.

Technical Skills Required in Data Science

The increase in the demand for data science roles has presented an enticing career path for many people. This includes professionals in the field and scientists who are obsessed with data and data science. It has therefore left them wondering what skills a data scientist is supposed to have to grow in this field. That’s especially for those who are not yet in the field but desire to do so.

  1. SQL Database

NoSQL and Hadoop are a large components of data science. Despite that, data scientists are still expected to write and execute queries in a structured query language.

SQL is a programming language. It allows professionals in this field to carry out operations like adding, deleting, and extracting data from databases. It also enables them to carry out analytical functions and transform database structures.

Data scientists seeking jobs through Only Data Jobs and other sites are expected to be proficient in SQL. That’s because the skill will enable them to access communication and work on data. It’s designed to give them insights when using it to query a database. It also has specific commands that allow you to save time and reduce the amount of programming needed to perform difficult queries.

Knowledge in SQL helps data scientists to better understand relational databases. It also helps to improve their profile as professionals.

  1. Unstructured Data

It’s crucial for data scientists to have the ability to work with unstructured data. This is the kind of data composed of undefined content and as such, cannot fit into database tables. That kind of data includes blog posts, customer reviews, video feeds, and audio.

A lot of this data is heavy text lumped together and as such, sorting it is difficult. That can be attributed to the fact that it’s not streamlined.

Unstructured data is often described as “dark analytics,” which is due to its complexity. Working with unstructured data enables data scientists to unravel useful insights in decision-making. Thus, professionals in this field ought to have the ability to understand and manipulate unstructured data.

  1. Knowledge of Programming Languages

Data scientists must be skilled in advanced statistical modeling tools. They must also have a deep understanding and knowledge of programming. This is in addition to a strong foundation in mathematics and statistics

There are various programming languages that data scientists must understand. They are:

  • Python. This language handles everything from data mining to the development of a website to run embedded systems in a single language. Pandas, for instance, is a Python data analysis package. This package can do everything from data importation in excel spreadsheets to plotting data with histograms and box plots.

  • R programming. R is a software package that includes functions for data manipulation and graphical display. Compared to Python, R programming is more widely used in academic environments. Machine learning algorithms can be executed fast and easily.

R Programming is specifically designed for data science needs, such as solving the problems encountered during data science processes. You should, however, note that R has a steep learning curve.

It is, therefore, difficult to learn, especially, if you have already mastered a programming language. Nonetheless, there are great resources on the internet that you can use to get started. One such resource is the simplilearn’s Data Science Training with R-programming Language.

  1. Skills in Data Wrangling and Data Exploration

Data wrangling is the action of cleaning and unifying complex data collections. Usually, the data received is not ready for modeling. It’s therefore important that a data scientist knows how to deal with the imperfections in data.

Data wrangling enables you to prepare data for further analysis. It enables you to transform and map raw data from one form to another. The knowledge in data wrangling enables you to reveal deep-lying intelligence within the data gathered from multiple channels. It also gives an accurate representation of actionable data.

Thanks to data wrangling, professionals in this field can reduce the processing and response time. It also helps to reduce the time spent gathering and organizing unruly data before it’s utilized.

  1. Probability and Statistics

Estimation and predictions are key procedures in data science. Probability and statistics are intertwined. This means that when the theory of probability is merged with statistical methods, data scientists can:

An understanding of various probability and statistical concepts is very important. Such concepts include:

  • The measure of variability

  • The population of sample data

  • Measurement level of data

  • Measurement of asymmetry

  • Measures of central tendency

  1. Data Visualization

Data visualization is a graphical representation of the findings from the data under construction. It enables data scientists to effectively communicate and lead the exploration to a conclusion.

Data visualization also gives data scientists the power to craft a story from data. This then leads to the creation of a comprehensive presentation.

Note that data visualization is an essential skill in data science. That’s considering that it’s not only about representing the final results but also about understanding data and its vulnerability.

Keep in mind that the visual portrayal of things is always a better idea. That’s because it helps to establish and understand the real value. Creating visualization enables you to get meaningful information.

Through data visualization, you can plot data for powerful insights. You can also determine relationships between unknown variables and visualize the areas that need improvement. The knowledge of data visualization helps with the identification of the factors that influence clients’ behavior.

  1. Multivariate Calculus and Linear Algebra

A lot of machine learning and invariable data science models are built with various unknown variables. That’s why a data scientist must be knowledgeable in multivariate calculus. Knowledge forms the cornerstone of a machine learning model.

Let’s now look at some of the topics of math that a data scientist must be familiar with;

  • Derivatives and gradients

  • The plotting of functions

  • Scalar, matrix, tensor, and vector functions

  • Maximum and minimum values of a function

  • Step function, sigmoid function, logic function, and rectified linear unit

  1. Database Management

Data scientists are unlike other professionals. They are masters of all jacks. That’s because they must be knowledgeable in mathematics, programming, statistics, visualization, and a lot more to be ‘full-stack’ professionals in their field.

A lot of work goes into preparing data for processing in an industry setting. But with the heaps and large chunks of data to work on, it’s crucial that these professionals know how to manage the data.

Remember that database management consists of programs that can edit databases. The programs can also index and manipulate the database. DBSM accepts the requests made for data from an application. It then instructs the OS to provide the specific data that are required. In large systems, a DBMS makes it easy for users to store and retrieve data.

Database management enables data scientists to:

  • Define, retrieve, and manage important data in a database.

  • It helps to define the rules to write, validate, and test data.

  • Supporting multi-user environment for easy access and manipulation of data in parallel.

  • Data scientists can manipulate data, its format, field names, record structure, and file structure.

  1. Machine Learning

Let’s assume that you work in an organization that manages and operates on large amounts of data and decision-making is data-centric. Such an organization will demand that a data scientist has the skills in machine learning.

Remember that just like statistics, Machine learning is a subset of the data science ecosystem. It contributes to the modeling of data and obtaining results. Machine learning for data science includes algorithms that are central to ML;

  • Random forests

  • Regression models

  • K-nearest neighbors

PyTorch and Tensor also find their usability in machine learning for data science. Machine learning enables data science to;

  • Planning of airline routes

  • Voice and facial recognition systems

  • Detection and management of fraud and risk detection

  • Comprehensive document and language recognition and translation

  1. Cloud Computing

Data science usually involves the use of cloud computing products and services. It makes it easy for data professionals to access the required resources as well as manage and process data.

The everyday responsibilities of data scientists include analyzing and visualizing data that’s stored in the cloud. You may be aware that data science and cloud computing go hand in hand. That’s because cloud computing gives a hand to data scientists using platforms like Google cloud and Azure. These kinds of platforms provide access to databases, programming languages, frameworks, and operational tools.

It’s a fact that data science is about the interaction with large volumes of data, considering the size and availability of tools and platforms. An understanding of the concepts of cloud and cloud computing is not only a pertinent but critical skill for data scientists.

Cloud computing enables data scientists to;

  • Mine data, analyze it, and summarize statistics

  • Tuning data variables and optimizing model performance

  • Validate and test predictive models and recommender systems

  1. Hadoop

We know that data science involves large-scale data analysis. It also involves exploring large datasets, mining them, and accelerating data-driven innovation. A data scientist must therefore learn Hadoop because it’s a popular open-source tool for managing and manipulating large datasets from multiple repositories.

A data scientist is supposed to be familiar with several Hadoop components. These components include:

  • Pig

  • Hive

  • Flume

  • Sqoop

  • MapReduce

  • Distributed File Systems

Being knowledgeable with experience in Hive and Pig is an excellent selling point for a data scientist. Experience in cloud tools such as Amazon S3 and Hadoop helps to add value to the knowledge base of your career as a data scientist.

  1. Apache Spark

This has become the most popular big data technology in the industry. It’s a big data computation framework just as Hadoop is. The difference, however, is that Spark is much faster compared to Hadoop. That’s because Hadoop reads and writes to disk which makes it slower. Spark, on the other hand, caches its computations in memory.

Apache spark is specially designed for data science. It helps professionals to run complicated algorithms much faster. It also enables them in disseminating data processing when dealing with a lot of data, hence solving them in a quicker and more efficient way.

With the help of Apache spark, data scientists can handle complex unstructured data sets. The skill can also be used on one machine or a cluster of machines.

Apache Spark makes it easy for data scientists to prevent the loss of data in data science. Note that the strength of Apache Spark lies in its speed and platform. This makes it easy to carry out data science projects. With this knowledge, you can carry out the analytics from data intake to distributing computing.

  1. Writing Efficient and Maintainable Code

Knowledge in this skill enables data scientists to:

  • Create algorithms to parse data

  • Gather and get the data ready through APIs

  • Directly deal with the programs that analyze, process, and visualize data

This skill is almost a given for data scientists considering that they are knee-deep in systems designed to analyze and process data. You must also understand the systems’ inner workings.

There are a lot of languages that are used in data science. As such, data scientists must learn and apply some of the languages that are relevant to their role, industry, and business challenges.

  1. Ability to Prepare Data for Effective Analysis

The ability to prepare data enables data scientists to;

  • It enables you to Source, collect, arrange, process, and model data for good use

  • It enables you to analyze large volumes of structured and unstructured data

  • It enables you to prepare and present data in the best form for better decision-making and problem solving

So what is data preparation? It’s the process of getting data ready for analysis. This includes data discovery, transformation, and cleaning tasks. Data preparation is an important part of the analytics workflow for analysts and data scientists alike. Regardless of the tool, you must understand data preparation tasks and how they relate to data science workflows.

  1. Microsoft Excel

MS. excel has been there even before any of the modern data analysis tools existed. So you can rightly refer to it as the oldest and most popular data tool. Today, however, there are multiple options that can replace MS. excel. But despite that, it has been proven that excel brings some surprising benefits compared to other tools.

It enables users to name and create ranges. It also allows you to sort, filter, and manage data. With excel, data scientists can create pivot charts, clean data, and look up for certain data among millions of records. This means that MS-excel isn't as outdated as some people might think.

It is, therefore, crucial for data scientists to have an in-depth understanding of Microsoft Excel. That’s because it enables you to connect to the data source and efficiently pick data in the desired format.

The skills in excel enable a data scientist to use VBA in developing macros. That is pre-recorded commands that can make routine, frequently-performed tasks easier for their human administrators.

Such tasks include updating the payroll, accounting, or project management. With Excel, a data scientist is also able to gain access to the Pivot Table. This is a tool that enables data scientists to quickly assess and distill conclusions from raw data.

  1. Social Media Mining

Social media mining refers to the excavation of data from social media platforms like Instagram and Twitter. Skilled data scientists can use the data to identify useful patterns. It can also be used to distill insights that a business can use in developing a greater understanding of an audience’s preferences and social media behaviors.

This type of analysis is important in developing an enterprise-level social media marketing strategy. Considering the importance of social media in business and its ability to stick around for the long term, developing greater social media data mining skills is a good idea.

Final Take Away

The field of data science is in great demand for professionals due to the growing amounts of data. It poses an alluring career path for those who enjoy working with data. But note that the world is already aware of the great potential of data science and as such, is crowding up in the marketplace.

Being a data scientist in the present day is exciting. Thus, it’s in your best interest to upgrade with the necessary skill sets. This will ensure that you don’t lose the race. We have discussed some of the key skills of a data scientist and we hope this helps you be a better professional.

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