What the world is experiencing right now can be labeled as an AI revolution that has further fueled data growth. Data is behind informed decisions for improving processes within businesses. Turning data into relevant information is not an easy task and only professionals with the right skills can do it. This calls for an increased need for experts with data science skills.
The global big data market will probably touch $273.4 billion dollars by the year 2026. This number is double of what it was in 2018. Big data will become bigger in the years to come and so will the demand for skilled, trained and experienced individuals. This article covers the top technical and non-technical data science skills to develop for a career in this field.
Data science is a field that combines specialized programming, machine learning, artificial intelligence, math, advanced analytics and statistics together. Experts explore gigantic amounts of organizational data to uncover meaningful and important details. It has a complete life cycle that includes the use of different tools, processes and roles.
A data science career is suitable for many different professionals. The list includes data scientists, data analysts, data engineers, business analysts, machine learning engineers and others. Getting started in this field means having to gain extensive knowledge and skills. This article is a complete guide to succeed in this field.
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There is quite a long list of the top technical data science skills one must gain. This list is relatively longer than other related technologies because of the immense scope for growth and expansion. Excelling at these skills will open the pathway to become a true professional in this field.
Data science is an exception where mathematical skills are must because it is not usually needed for general-purpose coding. Statistics, calculus, probability and algebra are the key mathematical areas of great importance here. Its knowledge need goes much deeper than just the basics.
One must have knowledge of the best and often used programming languages to communicate with computers. These languages give instructions to computers and are hence a must. There are hundreds of them out there but only a handful are apt for this field, including -
This general-purpose programming language is amongst the most important ones for data science. There is no end to Python features and benefits, all of which combine to make Python a leading name. Pandas, Matplotlib and NumPy are amongst its most popular libraries.
SQL is the acronym for Structured Query Language and is a domain-specific language. It interacts with databases and is often used alongside Python and R for editing and extracting data from other relational databases.
R programming is an open science language that caters especially to this field. It carries out a lot of tasks like machine learning, data visualization, data manipulation and statistical computing. Its large community makes it a preferred language amongst many professionals.
Experts in this area only need an understanding of the basic concepts of AI, ML and deep learning. Extensive learning is not needed as it is a separate specialization area. There are only a few overlapping aspects. This includes using different DL and ML models like predictive models and decision trees for data mining.
Natural language processing is an AI subset with a focus on bringing out important details from natural language and text. The data industry is increasingly using NLP with many of its techniques being the base for DL and ML power. Chatbots, recommendation systems and search engines are on the list.
The importance of data visualization for a professional in this field cannot be undermined. These experts communicate their findings through bar charts, tables, heat maps, scatter plots and other means. The purpose is to make sure that every responsible person (even non-technical ones) are able to comprehend the information. Power BI, Microsoft Excel and Tableau are the most popular ones.
Data mining is the process of gathering, segregating and analyzing humongous datasets. These data sets have a lot of additional and un-useful data with important insights here and there. Different data mining techniques like anomaly detection, linear regression analysis and clustering analysis sort and analyze this data.
The usual storage place for big data is the cloud through cloud computing. This means that the expert must have knowledge about interacting with the cloud and its basic working principles too.
Hadoop is one popular open source framework for processing gigantic data sets in an efficient manner. This is done through a network of different computers rather than a single one. Professionals whose job requires them to work with huge data sets on a regular basis will need this knowledge.
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No technical job could ever be completely fruitful without a strong base on non-tech skills. This list of the top soft data science skills is quite a handful because these professionals are unlike any other. They are multitalented and have a lot of information and ability.
Making sense of the data means understanding what it means and what its implications are. Knowing the data at hand will make it easier to analyze it. All this is only possible when the individual has a solid understanding of the business, the industry and the work.
Technology might be neutral but its use is not. Many data companies may develop and use practices that might impact other people and the society at large in an adverse manner. Ethical awareness is a must to keep the impact of data results positive. This means knowing concepts like feedback loops, data privacy and algorithm bias.
One has to also present the findings in a manner that is clear, concise and gets the message across. The results of the analysis must be understood clearly to make the entire process a hit and valuable. They should know about result communication in an effective manner through innovative approaches and frameworks.
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There is no point of knowing the important data science skills without working to earn them. It's a long way to go before becoming an expert in this field but it starts today. There are a few ways of gaining these skills and they are mentioned here.
This is a very technically advanced world wherein no one has to walk out of their homes to learn anything new. Online courses are just as useful and knowledgeable as an offline program. There are different options too and that includes interactive classes as well as self paced courses. An interactive class is great for learning in real time and being able to clear all queries. A self paced program is better when there's a tight schedule or time constraint.
YouTube is most certainly a goldmine for learning about anything and everything. One can step into this app and learn from untraditional means. There are many great tutors, experienced professionals and subject enthusiasts who are more than willing to impart their knowledge.
A good data science tutorial and a well written blog can reshape one's knowledge span about any topic. The internet is full of information for anyone willing to dive deep into the sea of knowledge. Different blogs cover different aspects and topics of this field.
Bootcamps offer a good amount of skill learning in a short amount of time while conferences keep one updated on the latest trends. Both are great for learning new things and can be taken both online or offline.
Practicing is a great way to get ahead of one's competitors in any given field and it's the same here. Pick a project that is comparatively easier to begin with and move on to more difficult ones. This field can only be excelled after a good span of working to getting into an internship or an entry level job are also good options.
Professionals in this field need to have a good number of skills and a very strong knowledge base to start. Earning all the important data science skills may take a while and definitely a lot of practice. Preparing for a job here means putting in long hours, dedication and sailing through in spite of all the hardships. Getting this toolkit under one's belt is a long shot and earned over a long period of time.
This field does require a person to have coding knowledge.
These professionals often have to work with SQL and so having its knowledge is needed.
Its future is quite bright because this field handles organizational data in all senses to bring out needed insights.
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