Top 10 Skills Employers Look for in Data Science Students

Top 10 Skills Employers Look for in Data Science Students

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It is true that the future is data science! So are you thinking about what skills you need for data science? This technology seems promising to play a pivotal role in shaping every industry to a greater extent. Since data science is the technology that converts data into meaningful insight. Even if the data is not useful, this technology will combine that data with other data points to bring a favorable and actionable insight that can prove to be very beneficial for a business. The field of data science is now at an all-time high. Different sizes of businesses (SMB, SME or Mid-Market and large enterprise) are looking for a way to leverage this data science to minimize business risks and maximize profits. So there is a lot of demand for data scientists at this moment. 

COVID-19 has brought significant disruptions to the way we live. Now almost everything has gone digital from learning to work. Learning the right skills has become very much important to thrive in the new world of work. And to be a successful data scientist, you need to boost your professional credentials. The best thing is that you will get a high salary and diversified exposure in this field. So have you got the skills that different companies and organizations look for in data science students? 

So, let’s take a look…

Thought Leadership 

If you want to be at the top of your career, then you must have thought leadership qualities. When you are a thought leader, then you will be regarded as a reliable source of information. And your ideas or opinions will influence others successfully. You need to have dedication and patience coupled with a strategy to become a thought leader in the data science domain. 

Mathematics & Statistics 

Mathematics and Statistics are the base in data science. The Mathematics subject will help to understand patterns and assist required to create algorithms. You must be thinking now what kind of math skills are required for data science? This includes Linear Algebra, Probability, Calculus, Graph Theory, and Gradient Descent. Statistics will help in collecting, organizing, analyzing, and interpreting data. This includes Mean, Median, Mode, Standard Deviation, Skewness and Kurtosis, Hypothesis Testing, Confidence Interval, and other Statistics fundamentals. 

Programming Knowledge

Having knowledge about programming is one of the essential skills for data science. Python and R are popular programming languages that you must know. But you have to learn programming languages like Excel, SQL, Python, Anaconda, NumPy, TensorFlow & Keras, PowerBI, etc. This is helpful for data analysis. 

Data Manipulation

Data Manipulation is the practice of inserting, deleting, and modifying and organizing data in a database. This helps to change data in such a way that it becomes easier to read and understand data. It uses a programming language called Data Manipulation Language (DML). This includes Scaling, Indexing, Merging, Transformation, and Imputing Missing Values. 

Data Analysis

Data analysis is one of the must-have skills for a data science career. It is the practice to obtain raw data, then organize, clean, and store it. This helps to implement various statistical techniques on data to get useful information, draw conclusions, and make the best decision on what is customizable as per the needs and requirements of businesses. This includes Diagnostic Analysis, Qualitative Analysis, Statistical Analysis, Predictive Analysis, and Quantitative Analysis. 

Data Munging 

Data Munging is the methodology of cleaning, extracting, and identifying data with the purpose of making it more valuable for analytics. It is an integral part of the data life cycle. R or Python packages are helpful in data munging. Data Munging is also known as Data Wrangling. 

Data Integration 

Data integration refers to the process of data consolidation from various types of sources with the purpose of providing a single view of the data. It is an integral part of the overall data management process that allows analyzing data for business intelligence. 

Data Exploration 

Data exploration refers to the first step of data analysis that allows us to create a more straightforward view of datasets along with their attributes. Culmination, Accuracy, Size, and Potential Connection Between Different Data Components, Data Files, or Data Tables come under attributes. 

Data Visualization 

Upgrading Data Visualization skills for improving your data science resume will surely get you into a well-reputed company. Data Visualization is an important part of the data life-cycle in the data science domain. It helps to translate large data sets and metrics into graphs, charts, plots, infographics, animations, and other visual formats. The main purpose of Data Visualization is to provide an easy way to see and understand complex data. Datawrapper, Tableau, Google Charts, and Kibana Will help to master this skill. 

Learn Advanced Technologies 

Along with Data Science, you should have knowledge of advanced technologies like Big Data, Business Analytics, and Machine Learning. Big Data is a huge volume of data, including structured, semistructured, and unstructured data, increasing with time. Business Analytics is the process of applying skills and using technologies to investigate the past performance of businesses to drive business planning by getting meaningful insight. Machine Learning refers to a part of Artificial Intelligence that enables devices to learn from data and make decisions like humans. 

Conclusion 

With data science disrupting the corporate world, most businesses require specialized data science professionals to grow their market value. It has become very important to upgrade your skills in the constantly evolving field of data science. So that you can help businesses with insightful solutions to take on business complexities and achieve goals.

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