The 6 key Data Science Skills Every Business Needs Today!

With the advent of the age of the internet where “Data is the new oil”, Data Science has become one of the most sought-after skill-set in the job market. According to Forbes, Artificial Intelligence (AI) & Machine Learning (ML) is at the top of the list of 25 technologies that are going to define this decade. Others in the list include the likes of – IoT, Augmented Reality, Big Data & Analytics, Blockchains & Distributed Ledgers and so on.

Data Science, in general, is an amalgamation of various processes, statistical as well as machine learning algorithms and systems to extract information from data. Most of the resources being open source, it is so easy to learn and hone as a Data Scientist. So, here are the top 6 Data Science skills that every business needs today.

1. Business Understanding

It is very crucial to understand the business thoroughly. Until and unless it is clear on how Data Science can improve the business; understanding of AI/ML algorithms, programming or any other skills for that matter will hardly be of any use. Be it Manufacturing, Retail or Healthcare, understanding the industry gives the data a context and introduces a perspective to the analysis.

2. Artificial Intelligence (AI) and Machine Learning (ML) Algorithms

Algorithms such as – Linear Regression, Logistic Regression, RandomForest, Neural Network are the backbones of Data Science. These algorithms provide not only a medium to understand the data but also to extract insights out of them. Comprehending the mathematics such as – probability distribution, statistical test, null value testing etc. behind these algorithms is vital to select the best strategy to follow. To understand which algorithm to apply where is the essence of working as a Data Scientist.

3. Programming Skills

Knowledge of at least one of the programming languages is essential to implement business problems using AI and ML Algorithms. Python, R, SAS, Java, Perl, C/C++ are few such coding languages. R and SAS had been the preferred languages, especially in the Banking and Finance Sectors so far. However, Python is fast becoming the favorite. There are standard packages such as Pandas, Scikit Learn, Tensorflow, Keras etc. available that can be leveraged in Python to implement AI and ML Algorithms without writing codes from scratch. Similar packages are available in R too. Besides, familiarity with cloud platforms such as – Azure, AWS, Google Cloud is also be beneficial.

4. Data Handling

Extracting information from structured and unstructured data is the main motive of Data Science. With the increase in the volume of data, experience in working with Big Data has become indispensable. Big Data Technologies such as NoSQL, Hadoop, Apache Spark in addition to writing complex SQL queries; is a prominent part of Data Science.

5. Data Visualization

– A Data Scientist should always be able to visualize the data. Visualization techniques are nothing but a graphical way of portraying complex information for easy understanding and consumption. Both Python and R have packages such as matplotlib, seaborn and ggplot that aids in plotting graphs. Also, there are many high-level Business Intelligence (BI) tools such as – Tableau, Power BI, Qlickview available in the market which can build and distribute interactive and shareable dashboards, depicting trends, changes and densities of data in graphs and charts. Understanding of at least one of these tools or packages is a good-to-have trade skill.

6. Story Telling

Finally, Storytelling is an integral skill-set that helps the business to communicate the solution backed with proper data. All of the above skills are of no use if the ultimate solution is not delivered effectively to the stakeholders. In short, an engaging, informative, captivating story that sticks with the audience is the core of making a difference.

Of Course, there can be so many other skills that can come handy while working in Data Science. After all, a Data Scientist is known as a rare breed of a unicorn, with unique combinations of skills that are hard to find. The skills noted above are no way an exhaustive set of skills. These are simply the fundamentals necessary to commence the journey with Data Science.

Happy Learning !!!