The demand for Data Science professionals is now at an all-time high. There are companies in virtually every industry looking to extract the most value from the heaps of information generated on a daily basis.
If you are reading this, you might harbor a great interest in data science, or you might have chosen Data Science as your career, which is one of the hottest skills in the market. Working as a Data Scientist is not an easy task. It requires a plethora of data scientist skills, and the knowledge of applying them in real work scenarios, and most of all, a quest for learning. The major reason why mastering data science might not be an easy task is the continuous development, research, and advancements being done in the name of Data Science, Machine Learning, and Artificial Intelligence. As a data scientist, you need to work with a variety of other professionals such as technical consultants, database administrators, stakeholders, IT admin, developers or testers, etc.
A data scientist is responsible for creating a story out of data, suggesting how the data can be used by an organization, and creating models that help an individual or an organization to take better decisions for growth. If you are looking for a career in data science, then KnowledgeHut Applied Data Science with Python Specialization Program is the right place to kick off your Data Science journey. In this article, we will discuss who is a data scientist and what are the different skills required for a data scientist to master the domain.
Let us discuss some of the data scientist skill sets that can be considered prerequisites whether Data Science is your cup of tea or not. Data Science is applicable to a variety of domains including healthcare, logistics, manufacturing, education, social media, etc. which makes it one of the areas which require an extensive amount of skillset. Linear Algebra as a skill can be termed as ground zero skill for data scientists. You do not require to master this subject but know a few concepts to help you understand the math behind machine learning algorithms. The data scientists of today were known as the statisticians of yesterday which make statistics sit on top of the data science skillset.
When you’re working with data, you would like to know how to perform some quick data processing, data manipulation, or data visualization tasks and all these can be done using a few clicks on Microsoft Excel, a good skill to have. If you master the must-have skills discussed in this article, then you are set to conquer challenges that are subsets of these skills.
This is the top skill for every Data Scientist to know if not master. It is the base for Machine Learning models as to why they do what they do. The data consists of several rows and columns. Each of these columns and its values can be represented as a vector. Linear Algebra teaches us the base of how the values are represented mathematically and from there how different operations are derived from them. The major concept that one should get hold of from Linear Algebra is the vectors and matrices concept, Eigen values, Eigen vectors, linear decomposition, etc.
There are some data science experts who might argue on whether this is an essential skill or can be termed as an optional one. but my sincere advice is to understand the fundamental concept behind the above concepts. There are chances that you might have learned this in high school so you might not want to skip something that you have already come across.
The second on the list is statistics. You must not be surprised by this as it is believed that it was the statisticians of the past who are now the data scientists. Statistics and Data Science go hand in hand. It covers the most crucial information about the data description, definition, distribution of data, type of data, and everything about data.
One must take this subject with complete dedication and understand each and every required fundamental. Statistics plays a crucial role when you need to understand or are answerable to why certain things are happening with data and your analysis. Statistics is not a small subject but what we need to understand is actually a cup of water from a bucket full of it. I will strongly recommend the book “Statistics without Tears” if you want to understand statistics in an easy tone.
If you have been staring at Excel as one of the data entry applications then let me tell you straight, IT’S NOT! MS Excel is much more powerful than you might know. It can act as your database (although not an efficient one for bigger data requirements), and help you work faster with your data for anything right from sorting, filling values, pivoting, or other complex tasks. It is a strong tool to create complex charts and graphs to understand your data. It provides a summary of the entries and can also do some surprising data analytics like running a linear regression model for you.
So, the next time you look at Excel, look at it as a Data Scientist and you will notice it is just the right tool for most of your pre-analysis that needs to be done for the data. The reason for having it at top of the list is that learning Excel through proper courses can help you work with it to its best potential and much faster than what you have been doing till now.
You are about to enter a field that demands you to make decisions irrespective of the fact that you are at a junior or a senior level. An excellent decision-making capability makes you stand apart from the crowd. Since school days, we are always told that we must always try to answer the questions without worrying if we were right or wrong.
But this is more to do with why we give that answer than what answer we give. It is our understanding that should reflect. Excellent decision-making is a part of a good understanding of the problem statement. It becomes important for you to be accountable for what you do with the data and that is where good decision-making skills come into play.
If you have crossed the must-have skills for a data scientist then you are placed well to start with the foundational skills that are required for the profile. These foundational data science skill sets define the starting point of your data science project lifecycle. Let us explore the foundational skills for every data scientist.
We have been talking about telling stories from the data but did not cover how exactly. If I were to tell you the last 5 Q-o-Q sales of a company and talk number, it will be hard for you to visualize how the performance of the sales has been in the period. But if I showed you some graphs which describe the trend of the sale then you can clearly understand the performance even without knowing the numbers. That’s how powerful these visualizations can be. The initial phase of any data science project goes into multiple rounds of discussions where we primarily get data understanding from the stakeholders. This data understanding and further data analysis when displayed in the form of graphs and charts speak strongly about them. But still, you might think why this is put up as a skill to learn.
Well, you cannot show a pie chart where a comparatively large number of entities are involved. A bar chart might prove useful for the scenario. Visualizations, no doubt are powerful but only if the right one is produced for a given situation about data. Some common visualizations include bar graphs, pie charts, line graphs, doughnut charts, maps, etc. If you like to code in python, you probably might make use of Matplotlib, seaborn, or plotly charting libraries. If you belong to a web development profile, then you must be aware of D3.js, Chart.js, or similar libraries. Even Excel can help you visualize data and make sense of it.
I would categorize Business Intelligence as an extension to Data Visualization since business intelligence is again bringing sense to business data with strong charts and graphs put together in the form of dashboards. Apart from data visualization, it requires you to have some expertise in the domain in which the business operates.
For some businesses, profit information might sit above the number of sales figures or vice-versa. This can only be understood with the help of gaining domain knowledge about the business. You try to bring intelligence to the specific business using visualizations so that the businesses can make better decisions based on the information that they see. Tableau is one of the best tools which can help you build quick and beautiful dashboards. Some of the other tools worth mentioning that can do the task are Power BI, Qlik, and again Excel.
If you are already thinking of talking to your clients or peers with beautiful dashboards then I would ask you to please take a step back. On a few occasions, you will find clean data where you can begin to create visualizations and show your insights and findings. The real-life data is not clean. If I ask you to fill out a form to view a website, you will probably start entering garbage values just so that you can proceed and view the contents of the page. That’s how I would describe the business data. In almost all the projects that I have been doing as a Data Scientist, the first encounter with data is not pleasant. Either the data have garbage values, blank records, or even unnecessary fields that even the client does not understand to. The data capturing strategy has not been the best until we understood its importance of it in this century.
That’s why now every company strives to collect data from its users or customers, and some even pay for it. Exploratory Data Analysis is that segment of Data Science that is the first step when you get hold of business data. It looks for such errors in the data and is probably a way to fix them if possible. It means to understand the data in and out so that when you start modeling the data, you actually know everything about your raw data. Visualizations are one way of analyzing this data.
There are other statistical methods (here’s where statistics starts showing up) as well which summarize the different characteristics and attributes shown by the data. It is believed that EDA can consume up to 90% of your total time so that the rest of the phases of development becomes easier. This can be termed the official documentation of your model.
Trust me, programming skills can really set the spotlight on you. It is one of the rare skills you might find among data science professionals. It is usually said that data science operations require you to know only a basic set of programming skills. I will absolutely deny it. In the short span of my career as a data scientist, I am in the limelight because of my excellent programming skills. It’s not what you do but how effectively you do it. You can wait for some extra days to get an item on your shopping wish list at the cheapest price.
Similarly, a good program effective both in terms of memory and processing will save you, if not immense then a good number of bucks since most of the things are cloud-based where we pay as we go. Here is the list of top programming skills I would like to recommend to every data science enthusiast.
This needs no introduction. Any job portal if you look for python, you will get loads and loads of job opportunities. It stands at the top among the top programming skills needed for data scientist, followed by R language. It is easy and fast to learn, and quick to implement. I did not include R language for a reason. It is used widely but not as intensive as python. There is nothing python cannot do and R can but definitely, there are a lot of things python can do and R cannot which will always give python an upper edge. But learning python is not only about basics such as literal, data types, functions, etc. There is more to it.
Everyone knows the basics and just stops, at least that is what I have been seeing these days. Earlier programming was all about Object Oriented concepts, Data Structures, and Algorithms and it still is but not everyone seems to be interested to learn them. If you are learning python and do not learn these advanced concepts, don’t stop. Keep going. It will definitely take time to get hold of all this but slowly you should be moving.
Data Structures and Algorithms can wait but do play around with OOPS methodology in python if you want to write an effective code. Your program is not about achieving the end results but you should also be concerned with optimizing it and thoroughly testing it. These skills need to be learned continuously to make yourself a better programmer. A master does not only know the basics well!
Yes, Flask is a framework in python but I have mentioned it explicitly in the programming skills for a reason. You will always need to find a better way of deploying your models and if you know python, there is no better way of doing that other than Flask. It is super quick and easy to start by exposing or deploying your model using APIs built on the Flask framework. You will find tons of resources on how to use flask and deploy it on cloud platforms.
Data Warehousing is storing your data from multiple sources at a unified location so that it can be used for reporting and analytics. It is not a programming skill but while you learn Structured Query Language or simply SQL (seq-uel) you should be knowing the definition of data warehousing. This way some things will make more sense to you as to why we are doing what we are doing. SQL is regarded as one of the easier languages to learn and a must-known skill for every programmer or developer.
It does not consume a lot of time to learn and also just the intermediate skills will do. Knowing SQL also allows you to design better data storage architecture especially if you are using relational databases. The best part about SQL is almost all of the database management systems speak the same language syntactically.
As a data scientist, you are probably not working with small data. Working with huge amount of data or Big Data is very common. It is very likely that you might come across cases where you require something which is better than Python or R ecosystem while working with Big Data. Apache Spark comes to the rescue.
According to some of the research, you can find over the web, Apache Spark is getting seemingly popular among organizations to cater to their big data processing needs. It is an open-source, distributed processing system used for big data processing. Top companies like Uber, Microsoft, Oracle, etc. have adopted this technology. The official language of Spark is Scala. It also supports Python, Java, and R programming languages. There is a lot to talk about Spark and its advantages with Big Data.
R is altogether a new programming language for statisticians. Anyone with a mathematical bent of mind can learn it. Nevertheless, if you do not appreciate the nuances of Mathematics then it’s difficult to understand R. This never means that you cannot learn it, but without having that mathematical creativity, you cannot harness the power of R.
The data scientist's technical skills consist of varied skills that cater to different aspects of being a data scientist. The primary technical skill you will need is machine learning and neural networks. Secondary technical skills include Hadoop for Big Data and Cloud Computing basics to walk with the current technological trend.
We know the importance of machine learning in data science and artificial intelligence. If you do not master this, you can never master the art of data science. It is undoubtedly the essential skill in the data science list. However, there are continuous advancements in this field so it is nearly impossible for someone to stay updated with the latest research and findings in detail.
But definitely, there are certain set of basic and intermediate algorithms that one should know, namely, linear regression and its types, logistic, K-nearest neighbors, Naïve Bayes, decision trees, random forests, etc. Here, the linear algebra would come in to play to understand a few of the algorithm’s working. Machine Learning helps to forecast the future trends, automate clustering, etc.
If you have already put your shoes in machine learning then here is the way to go next. It is strongly suggested that only if you know the base algorithms of machine learning really well, you should start learning about neural networks. It is the base to artificial intelligence since the algorithms implemented through neural networks usually try to mimic the human brain. Therefore, these algorithms are extremely powerful if implemented well. An example of neural network is voice assistants like Siri or Google.
Apache Hadoop is a collection of open-source software utilities that solves problems involving massive amounts of data and computation. It is based on the MapReduce architecture and is a must have skill if you are entering the world of big data. You might or might not use the Hadoop framework but knowing about how it works and the purpose of its existence is a must. It is based on the Apache license and is one of the ways you can implement Apache Spark.
Cloud computing platforms sit at the forefront when it comes to build and deploy your machine learning models. As a data scientist, you do not need to know everything about cloud computing architecture and about every cloud service provider. It is mostly handled by IT admins. What you can put under your belt is what exactly is cloud computing all about and how it differs from the traditional on-premise architecture, handful of knowledge about deploying your models on cloud platforms and using some Linux commands. Amazon’s AWS is the most widely used cloud platform followed by Azure by Microsoft.
Data Science is not only about knowing coding, statistics, and machine learning algorithms. Definitely, there is more to it. Your expertise will vary from business domain to domain. Someone who is good at producing forecasts of sales might not produce good chatbots. The domain expertise required in both the problem statements is different. There are a lot of different business domains that data scientists work in like Healthcare, Supply Chain, Finance, Metals, Automobiles, Power, and many more. I believe it is important that we define at least two domain of expertise so that we are prepared when an opportunity arises.
We have mentioned the two frequently demanded skills based on my experience of the different kinds of projects that I have come across, you are free to choose if you are inclined to some other domain.
Forecasting the future stock prices, metal prices, or even the number of sales is gaining popularity since it brings monetary gains to an organization. Timeseries modelling is all about forecasting or predicting future trends or behavior by analyzing past trends. It requires you to know a different set of algorithms that you will specifically not come across while learning the machine learning algorithms.
Knowing time-series modelling concepts can help you cater to a wider industry where forecasting certain commodities prices or sales figures is required. Better forecasting capabilities can help a portfolio manager to rebalance portfolio to maximize gains, a metal industry to stock up raw materials by forecasting the best duration to buy them, an e-commerce platform to predict the increase in sales and accordingly, the design offers or even manage website load.
Again, natural language processing is used in a variety of businesses and does require some additional understanding of the same apart from the essential set of skills. Knowing about Natural language processing or NLP will help you to work with text data, different from any of the things mentioned earlier. It is a base skill required to create chatbots, voice assistants, auto-generating captions or titles, etc.
What if you clearly understand what’s happening but are not able to explain any of it? In this section, we will cover the non-technical skills needed for data science.
Data governance and security are the responsibility of every data scientist. Most of the data, we, as data scientists work with are confidential and it is our responsibility that adheres to all the rules to be followed to maintain the integrity and confidentiality of the data. Breachlevelindex has reported a daily loss of five million data records which amounts to the loss of 60 records per second. One should take account of the data they are working with to avoid the data getting exploited. This can be taken care of by following the best practices as adhered by the organization and using proper data sharing mediums.
It’s good to see your recent model version gives you some extra percentage of accuracy, but how? What was my accuracy earlier? What parameter did you change? Did you use the same model? Have you documented it? Is it to do with the use of a different library? And many questions like this can be answered without actually having you to speak. With Machine Learning Model Operationalization Management (MLOps), you provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.
It has laid down the principles or rules that one should follow during your complete machine learning model development lifecycle which includes model revisions as well. These set of rules and principles can be found on their official website. There are a lot of tools and libraries that can help you build an MLOps-proof development framework. For example, if you are working with python then an open-source library - mlflow can help you manage your machine learning lifecycle by following most of the MLOps principles.
As I already said, if you are not able to communicate well enough, especially with your clients then you might face hard times. Non-technical people will not understand what is a normal distribution or what a random forest does. You have to always look at intuitive ways to make them understand without using any technical analogies.
Where there is data, there is accuracy is not true, there are more things that need to be considered. One needs to have a strong command over how well one can convince the person on the other side of the table if he or she makes unrealistic demands. At the same time, you should be in a good position to confidently suggest changes that can bring value to the organization. All these require communication skills as one of the must-have data scientist skills.
Data scientists need to keep their brains racing with critical thinking. They should be able to apply the objective analysis of facts when faced with a complex problem. Upon reaching a logical analysis, a data scientist should formulate opinions or render judgments.
Data scientists are counted upon for their understanding of complex business problems and the risks involved with decision-making. Before they plunge into the process of analysis and decision-making, data scientists are required to come up with a 'model' or 'abstract' on what is critical to coming up with the solution to a problem. Data scientists should be able to determine the factors that are extraneous and can be ignored while churning out a solution to a complex business problem.
Before arriving at a solution, it is very important for a Data Scientist to be very clear on what is being expected and if the expected solution can be arrived at. It is only with experience that your intuition works stronger. Experience brings in benefits.
If you are a novice and a problem is posed in front of you; all that the one who put the problem in front of you would get is a wide-eyed expression, perhaps. Instead, if you have hands-on experience of working with complex problems no matter what, you will step back, look behind at your experience, draw some inferences from multiple points of view and try assessing the problem that is put forth.
In simple steps, critical thinking involves the following steps:
A majority of Data scientists already have a Master’s degree. If Master’s degree does not quench their thirst for more degrees, some even go on to acquire Ph.D. degrees. Mind you, there are exceptions too. It isn’t mandatory that you should be an expert in a particular subject to become a Data Scientist. You could become one even with a qualification in Computer Science, Physical Sciences, Natural Sciences, Statistics, or even Social Sciences. However, a degree in Mathematics and Statistics is always an added benefit for an enhanced understanding of the concepts.
Qualifying with a degree is not the end of the requirements. Brush up your skills by taking online lessons in a special skill set of your choice — get certified on how to use Hadoop, Big Data or R. You can also choose to enroll yourself for a Postgraduate degree in the field of Data Science, Mathematics or any other related field.
The Data Scientists of the modern world have a major role to play in businesses across the globe. They have the ability to extract useful insights from vast amounts of raw data using sophisticated techniques. The business acumen of the Data Scientists helps a big deal in predicting what lies ahead for enterprises. The models that the Data Scientists create also bring out measures to mitigate potential threats if any.
As a Data Scientist, you may have to face challenges while working on projects and finding solutions to problems.
If you are a Data Scientist, you are expected not just to study the data and identify the right tools and techniques; you need to have your answers ready to all the questions that come across while you are strategizing on working on a solution with or without a business model.
Organizations vouch for candidates with strong business acumen. As a Data Scientist, you are expected to showcase your skills in a way that will make the organization stand one step ahead of the competition. Undertaking a project and working on it is not the end of the path scaled by you. You need to understand and be able to make others understand how your business models influence business outcomes and how the outcomes will prove beneficial to the organization.
And a Data Scientist is expected to be adept at coding too. You may encounter technical issues where you need to sit and work on codes. If you know how to code, it will make you further versatile in confidently assisting your team.
The world does not expect Data Scientists to be perfect with knowledge of all domains. However, it is always assumed that a Data Scientist has the know-how of various industrial operations. Reading helps as a plus point. You can gain knowledge in various domains by reading the resources online.
To be a successful Data Scientist, you should be able to explain the problem you are faced with to figure out a solution to the problem and share it with the relevant stakeholders. You need to create a difference in the way you explain without leaving any communication gaps.
Now that we have already discussed the top skills required to become a master of data science, it is time to get started with them one by one. The journey is definitely long but fruitful because data science is not going anywhere at least for the next few decades. There is a lot of things that have been done as part of research in this domain and yet there is a lot of scope for further research which is still being done. Many companies nowadays are redefining their data collection strategies and focusing on how efficiently they can collect as much data as possible. While we are still here, there is a lot to uncover in the coming years.
Among the 20 skills mentioned in the article, statistics, python, data visualization, exploratory data analysis (EDA), and machine learning are the top 5 skills to begin with.
Most of the data scientist roles are consultant based or require frequent interaction with the client, therefore, client handling or communication is the must-required soft skill for every data scientist.
You can get to know the basics about the Data Science stream through various free resources like videos, blogs, etc. but it requires proper guidance to land on a proper data science profile. With industry-aimed programs designed by experts, hackathons, training, and mentorship, hands-on learning, KnowledgeHut can provide you with the right career advice. Check out our course on applied data science with python specialization and work on real-life science projects.
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