Top 10 Data Scientist Skills You Should Know

Required skills you need to know to become a data scientist

sonia jessica
Sep 29 · 11 min read

Who is data science for?

Data science is an interdisciplinary approach to obtaining useful insights from today’s organizations’ massive and ever-increasing volumes of data. Preparing data for analysis and processing, undertaking advanced data analysis, presenting the results to expose trends, and allowing stakeholders to make educated decisions are all part of data science. Data science is a discipline that combines domain knowledge, programming abilities, and math and statistics knowledge to extract useful insights from data.

Data science is one of today’s most fascinating subjects. But, why is it so crucial?

Because businesses are sitting on a gold mine of data. Data volumes have expanded as contemporary technology has facilitated the creation and storage of ever-increasing amounts of data. 90 percent of the world’s data was created in the last two years, according to estimates. Every hour, for example, Facebook users post 10 million images.

The vast amounts of data collected and saved by these technologies have the potential to revolutionize businesses and communities all around the world — but only if we can understand it. This is where data science enters the picture.

Data science identifies patterns and generates insights that businesses may utilize to make more informed decisions and develop more innovative products and services. Perhaps most crucially, it allows machine learning (ML) models to learn from the massive volumes of data that they are fed, rather than depending solely on business analysts to figure out what they can from the data.

Data science remains one of the most promising and in-demand career pathways for qualified individuals. As a result, it is the most lucrative employment of the twenty-first century.

What does a data scientist do?

Data Scientist Skills

Data scientists have become indispensable assets in almost every corporation during the last decade. These professionals are well-rounded, data-driven individuals with advanced technical capabilities who can construct complicated quantitative algorithms to organize and synthesize vast amounts of data in order to answer questions and drive strategy in their company. This is combined with the communication and leadership skills required to provide tangible results to numerous stakeholders throughout a company or organization.

Data scientists collaborate closely with business stakeholders to learn about their objectives and how data may help them achieve them. They construct algorithms and prediction models to extract the data that the business needs, as well as help, evaluate the data, and share findings with peers. While each project is unique, data scientists carry out the following steps:
To get a better understanding of the problem, they ask the appropriate questions.
Gathering business requirements and related data from various sources.
Process, clean, integrate and store the data.
Exploratory data analysis (EDA) to figure out how to deal with missing data and to seek trends and/or possibilities.
Following the exploratory phases, the cleansed data is exposed to a variety of algorithms, such as predictive analysis, regression, text mining, pattern recognition, and so on, depending on the requirements.
Use excellent data visualizations and reports to communicate predictions and findings to management and IT teams.
Changes to existing procedures and techniques that are cost-effective are recommended.

Required skills to become a data scientist

Today’s effective data professionals recognize that they must go beyond the traditional abilities of large-scale data analysis, data mining, and programming. Effective data scientists are able to develop relevant questions, acquire data from a variety of sources, organize the data, translate results into solutions, and present their findings in a way that favorably influences business decisions. Because these talents are required in practically every industry, skilled data scientists are becoming increasingly valuable to businesses.

So, in this article, I am mentioning the top 10 data scientist skills that you must know.

1. Fundamentals of data science

Understanding the principles of data science, machine learning, and artificial intelligence as a whole is the first and most crucial talent you’ll need. Understand issues such as the difference between deep learning and machine learning, terminologies, and terms that are most commonly used, the difference between supervised and unsupervised learning, and others. To work as a Data Scientist, you’ll need knowledge of popular data science technologies like Microsoft Excel, Python or R, Hadoop, Spark, Tableau, and others.

2. Machine learning

Machine learning is a must-have ability for any data scientist. Predictive models are created using machine learning. If you want to forecast how many clients you’ll have in the upcoming month based on the previous month’s data, for example, you’ll need to employ machine learning techniques.

A vast number of data scientists lack expertise in machine learning techniques and topics. Neural networks, reinforcement learning, adversarial learning, time series, natural language processing, outlier detection, computer vision, recommendation engines, and other techniques are examples of this. If you want to set yourself apart from other data scientists, you need to be familiar with machine learning techniques including supervised machine learning, decision trees, and logistic regression, among others. These abilities will aid you in solving a variety of data science challenges based on important organizational outcomes projections.

3. Probability and statistics

Data Science is the process of extracting knowledge, insights, and making educated decisions from data using various methods, algorithms, or systems. Making conclusions, estimating, and predicting are all crucial aspects of Data Science in this scenario.

Before you can create high-quality models, you need to understand statistics. Machine Learning begins with statistics and progresses. Even the concept of linear regression is a statistical analysis concept that has been around for a long time.

It is necessary to understand the concepts of descriptive statistics such as mean, median, mode, probability distributions, sample and population, CLT, skewness and kurtosis, and inferential statistics, such as hypothesis testing and confidence intervals.

4. Programming knowledge

It is one of the requisite skills for Data Scientist positions. Writing computer programs and analyzing vast datasets to find solutions to complicated issues is what programming is all about. Data scientists must be able to write code in a range of languages, including Java, R, Python, and SQL.

Python, R, and Julia each have their own set of advantages and disadvantages. R is a statistical analysis and visualization language, whereas Python is a general-purpose programming language with various data science packages and rapid prototyping. Julia combines the finest of both worlds while also being speedier.

All of the core skills needed to transform raw data into useful insights are gathered in Programming Skills for Data Science.

5. Data visualization

One of the most significant aspects of data analysis is data visualization. It has always been critical to convey information in a way that is both understandable and pleasant to the eye. One of the skills that Data Scientists must acquire in order to connect more effectively with end-users is data visualization.

To begin, you must be comfortable with basic plots such as histograms, bar charts, and pie charts, before moving on to more advanced charts. During the exploratory data analysis stage, these graphs are extremely useful. Colorful graphics make univariate and bivariate studies much easier to comprehend.

As a data scientist, you’ll need to be able to visualize data using tools like ggplot, d3.js, and Matplotlib, as well as Tableau. These tools will assist you in converting complex project outcomes into a format that is easy to understand.

It helps in determining what influences customer behavior, quarterly sales mapping, client reporting, personnel performance, and creating a marketing plan that is tailored to specific user groups, and so on.

6. Big data tools

One should have a working knowledge of Big Data tools like Apache Spark, Hadoop, Talend, and Tableau, which are utilized to deal with massive and complex data that typical data processing applications can’t handle.

Organizations have been overwhelmed by such a big volume of data, and they are attempting to deal with it by fast embracing Big Data Technology so that it can be properly stored and used when needed, thereby giving them an advantage over their competitors.

7. Deep learning

It is a type of Machine Learning that is more advanced. Deep Learning models are being used by every company nowadays since they have the capacity to overcome the constraints of standard Machine Learning methodologies. Fundamentals of Neural Networks, the libraries used to create Deep Learning models such as Tensorflow or Keras, and how Convolutional Neural Networks, Recurrent Neural Networks, RBM, and Autoencoders function are among the other abilities required for Data Scientist positions.

Deep Learning has made all of this feasible. Because of advances in data storage capacities and computational advancement, it is a high-growth area in the field of Artificial Intelligence.

To succeed in this sector, you must have a strong understanding of programming (ideally Python) as well as linear algebra and mathematics. You can begin by developing basic models and progress to more complicated models such as CNN, RNN, and others.

8. Data manipulation

Data manipulation is one of the most important abilities for a Data Scientist. It entails the process of altering and organizing material in order to make it more readable.

Data manipulation, often known as wrangling, is the process of cleaning and transforming data into a format that may be properly evaluated in subsequent phases.

Data processing and wrangling, on the other hand, can take a long time but can ultimately help you make better data-driven judgments. Missing value imputation, outlier treatment, correcting data types, scaling, and transformation are some of the common data manipulation and wrangling techniques used.

9. Soft skills

Companies looking for a competent data scientist want someone who can communicate their technical findings to a non-technical team, such as the Marketing or Sales departments, in a clear and fluent manner. In order to manage the data effectively, a data scientist must enable the company to make decisions by providing them with quantitative insights, as well as knowing the demands of their non-technical colleagues.

A data scientist’s most important acquired talent is storytelling. It is the skill of constructing a coherent storey around numbers that transmit logic, strike the proper chord with stakeholders, and persuade them with sufficient arguments to drive a choice.

Data Science is still evolving, and let me tell you something important: in this profession, learning never ends. One day you master the tool, and the following day it is trampled by a more complex instrument. A data scientist must be inquisitive and eager to learn.

10. Business acumen

While Python programming, SQL querying, and data visualization are the essential technical abilities required of a data scientist, the need for excellent business acumen cannot be overstated. To obtain a thorough grasp of the business challenge and build a solution, it is necessary to have industry-specific knowledge. Basic business knowledge on rules such as minimum age criteria for credit cards, loan quantum for a mortgage as defined by regulatory authorities, compliance, and regulations, knowledge of accounting standards and risk management, and so on are industry-specific knowledge if you work in the finance domain.

You must fully comprehend the company’s primary objectives and goals, as well as how they affect your work. You must also be able to develop solutions that achieve those objectives in a cost-effective, simple-to-implement and widely adopted manner.

What do recruiters look for when hiring

A recruiter looks for a specific set of abilities while looking for a Data Scientist who will be with the organization for a long time. The goal of any recruitment process, whether it includes unique rounds, a live project, or a standardized interview, is to find a Data Scientist who can demonstrate extraordinary skills. These data scientist talents will define the scope of future challenging assignments he can tackle. As a candidate for Data Science, you must demonstrate streaks of your knowledge and skills in the most effective yet subtle way possible.

A data scientist is someone who has worked in a variety of fields. They use artificial intelligence and machine learning to find patterns and trends and generate data-driven forecasts. They must have a good foundation in artificial intelligence, machine learning, statistics, and data engineering, among other areas.

Data scientists must be inquisitive and results-driven, with great industry-specific expertise and communication abilities that enable them to convey highly technical outcomes to non-technical colleagues. To create and analyze algorithms, they have a solid quantitative background in statistics and linear algebra, as well as programming experience with a focus on data warehousing, mining, and modeling.

How to become a data scientist

To become a data scientist, follow these three steps:

  • Obtain a bachelor’s degree in information technology, computer science, mathematics, physics, or a related discipline;
  • Obtain a master’s degree in data science or a closely related discipline;
  • Obtain experience in the field in which you wish to work (ex: healthcare, physics, business).

If you want to work in a senior leadership role, you’ll need to get a master’s or doctorate degree.

Data science degrees are available at several schools, which is an obvious choice. This degree will teach you how to process and analyze a large amount of data and will include a lot of technical material on statistics, computers, and analysis methodologies, among other things. Most data science programs will also have a creative and analytical component, which will help you to make decisions based on your results.

While a data science degree is the most obvious approach, technical and computer-based degrees can also help you get started in data science. The examples of degrees that can help you learn data science are Computer science, Statistics, Physics, Mathematics, Economics, and others.

Conclusion

Scope of data science and making a career in it

The world is changing in response to current trends, and Data Scientists are one such trend in the modern world. It’s one of the most popular professional choices among today’s youth. Every business, from large corporations to small startups, requires a Data Scientist to make appropriate use of the massive amounts of data it generates and keeps. Data Science has a wide range of applications in both current and future circumstances. The following are a handful of the most prevalent job titles for data scientists: business intelligence analyst, data mining engineer, data architect, data scientist, and senior data scientist.

Artificial intelligence, machine learning, and robots, among other innovative technologies, are likely to rule the world in the coming decade. The coronavirus epidemic has accelerated the digitalization of businesses, altered labor dynamics, and made space for remote working, which has become the new normal across industries.

Companies of all sizes and industries are looking for expertise to help them wrestle big data into submission, from Google, Linked In, and Amazon to the basic retail store. In some companies, “new look” data scientists may be in charge of financial planning, ROI evaluation, budgeting, and a variety of other management-related responsibilities.

Since 2012, the Data Science industry has grown rapidly. As more companies turn to machine learning, big data, and AI, the demand for data scientists is growing. By monitoring items around one’s house or workplace, improving the quality of online shopping, enabling safe online financial transactions, and many other things, data science has made everyday life easier.

Data science is a vast professional path that is always evolving, promising a plethora of options in the future. Job responsibilities in data science are projected to become more specialized, leading to specialties in the subject. People who are interested in this field can take advantage of their opportunities and pursue what best suits them by using these standards and specialties.

Businesses and enterprises collect data on a daily basis for transactions and online interactions. Many businesses have the same problem: analyzing and categorizing the data they collect and store. In a case like this, a data scientist becomes the savior. Professionals with insight into how a business may thrive with data-driven ideas and implementation have a particular niche and need. Experts in data science are needed and valued in practically every sector. Big data is used by many organizations and even governments to provide efficient services to its clients. The data science craze isn’t going anywhere anytime soon.

As a data scientist, you’ll never be bored if you appreciate tackling complicated, real-world problems. Your major role is to analyze and process massive amounts of raw data in order to find answers and insights.

As a data scientist, you’ll have a bright professional outlook if you have the necessary qualifications. Individuals with these talents will continue to be in high demand, and those now working in data science will see their incomes rise in the future. As the need for qualified people to fill these positions grows, so will the incomes offered — even the lowest-paying data scientist employment will still provide a comfortable living.

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