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Friday, May 20, 2022

Which Data Science Skills Are Important?

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Data science is considered one of the most sought-after and popular emerging domains. LinkedIn reports that Data Science dominates its emerging job ranking with a whopping 37% hiring growth over the past three years. Data science is gaining popularity day by day as it is revolutionizing almost every sector of business. We know that Data is a precious asset for any organization but it is meaningless until its conversion into valuable information. Data science is affecting our daily routine lives and activities such as maintaining our personal and official data, paying our bills, assisting us through Siri and Alexa, supporting us with driverless cars, and providing us with infinite information within a  few seconds.

Data science is a vast field that involves many streams like Computer science, Inference, statistics, Predictive analysis, Machine learning algorithms, and many new technology and tools to analyze and draw meaningful insights from data. The importance of data science reflects in the many products designed to boost customer experiences. So there is a high demand for skilled data analysts and data scientists. Therefore many reputed training providers offer graduate and post-graduate programs in data science to help candidates land their careers in the evolving field of data science.

Here in this article, we are going to discuss some skills that are important to learn to become a successful professional in data science.

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What is Data Science?

Data science is known as a multidisciplinary field that includes scientific methods, algorithms, processes, and systems to extract insights and hidden knowledge from massive structured and unstructured data. Basically, data science is related to machine learning, data mining, and big data. This drastic field involves data analysis, statistics, informatics, and other advanced related methods to understand and analyze actual relations with data.

It is also known as a study domain that deals with a huge volume of data through modern techniques and tools to find hidden trends and patterns, meaningful information, and make data-driven business decisions. Data Science often uses complex machine learning algorithms to create predictive models. Several organizations have started implementing data science techniques to scale their business and grow their customer’s satisfaction.

Data science follows a specific life cycle that has five different stages and each stage has a different aim and task.

  • Capture Stage- It involves Data entry, Data Acquisition, Signal Reception, Data Extraction, and collection of data.
  • Maintain Stage- It involves Data cleansing, Data warehousing, data processing, Data staging, and data architecture, which covers taking the raw data and making it useful for further use.
  • Process Stage- This stage involves Data mining/classification/clustering, data summarization, Data modeling, etc. The main work in this stage is to prepare data and find patterns, ranges, and determine how useful it can be in predictive analysis.
  • Analyze stage- It involves Predictive analysis, Exploratory and confirmatory, Text mining, Regression, and Qualitative analysis. It also involves performing several analysis processes on data.
  • Communicate Stage- This stage involves Data reporting, Business intelligence, data visualization, and Decision Making process to prepare analysis reports in the form of charts, graphs, dashboards, etc.

Some Important Data Skills to Learn

Becoming a successful data scientist requires a combination of technical and soft skills. The following mentioned are some important and effective data science skills.

  • Educational Qualifications- First of all in any field fundamental qualifications are required and so is data science. It needs a strong knowledge of statistics and mathematics. Commonly Computer science, mathematics, statistic, and engineering are fields of study in data science. But this domain often relies on practical projects and portfolio work to signal their value to employers as it does not have a set of industry-recognized certifications.
  • Basic Knowledge of Data Science- To start a journey always required some fundamental knowledge. The data science field also required fundamental knowledge of data science, machine learning, and artificial intelligence as a whole to develop and explore skills. You must have some knowledge of deep learning and machine learning, common tools, and terminologies, knowledge of the difference between business analytics, data science, and data engineering, etc.
  • Probability and Statistics- To become a skilled data scientist statistics and probability are the must-required skills to learn. It helps produce high-quality models. Knowledge of descriptive statistics, probability distribution, sample and population, hypothesis testing, CLT, inferential statistics is also required.
  • Knowledge of programming languages-  An expert data scientist requires a deep knowledge of programming languages such as Python, R, Hadoop, SQL, Julia, etc. These languages provide a way to communicate with machines. Choose your programming according to your requirements and master your skills.
  • Data Analytics and Data manipulation-  Data manipulation (data wrangling) and Data analytics can create great machine learning projects. Data manipulation helps you clean the data and transform it into a format to analyze it in the next levels. On the other side data analytics is the step where you can understand all about data deeply.
  • Machine Learning and Artificial Intelligence- These skills are also considered the crucial skills to become a skilled data scientist. It helps analyze large chunks of data using data-driven models and algorithms and can automate significant parts of a data scientist’s job. Data scientists must also know about advanced machine learning techniques like supervised and unsupervised machine learning, logistic regression, decision trees, natural language processing, outlier detection, etc.
  • Data Visualization- Having technical knowledge is a must but presenting your researches, work, and reports in an attractive and easiest way is also important. Here comes the role of data visualization skills. It is known as the graphical presentation of data using visual effects such as graphs, charts, infographics, maps, etc. Data visualization comes between technical analysis and visual storytelling. Data visualization has become a key tool in making sense of huge volumes of data every day.

Besides these skills, there is a huge variety of skills that a data scientist should learn to stay ahead of their competitors. Some of the soft skills (interpersonal skills) such as communication skills, storytelling, learning skills, collaboration skills, leadership skills, analytical thinking, teamwork are also required to work great as a data scientist. So start preparing today to earn the data science skills and take your career ahead.

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