14 Must-Have Skills to Become a Data Scientist

The 14 Must-Have Data Science Skills

• Fundamentals of Data Science
• Statistics
• Programming knowledge
• Data Manipulation and Analysis
• Data Visualization
• Machine Learning
• Deep Learning
• Big Data
• Software Engineering
• Model Deployment
• Communication Skills
• Storytelling Skills
• Structured Thinking
• Curiosity

 Data Science Skill #1: Fundamentals of Data Science

As a newcomer in data science, I did what everyone around me did – started applying machine learning techniques like linear regression and SVM without even understanding the basics. I believe it’s all a fault of the generic “Build your machine learning model in 5 Lines of code” but this is miles away from reality.

The first and foremost important skill you require is to understand the fundamentals of data science, machine learning, and artificial intelligence as a whole. Understand topics like –

1. Difference between machine learning and deep learning
2. Difference between data science, business analytics, and data engineering
3. Common tools and terminologies
4. What is supervised and Unsupervised Learning
5. Classification vs regression problems

Data Science Skill #2: Statistics and Probability

Statistics is the grammar of data science.

When you start learning to write sentences, you must be familiar with grammar to build the right sentences similarly statistics is an essential concept before you can produce high-quality models. Machine Learning starts out as statistics and then advances. Even the concept of linear regression is an age-old statistical analysis concept. 🙂

The knowledge of the concept of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Then come the various probability distributions, sample and population, CLT,  skewness and kurtosis, inferential statistics – hypothesis testing, confidence intervals, and so on.

Statistics is a MUST concept to become a data scientist. You can deep dive into some of these concepts with these clear articles and their examples –

1. Statistics for Data Science: What is Normal Distribution?
2. Statistics for Analytics and Data Science: Hypothesis Testing and Z-Test vs. T-Test –
3. Statistics for Data Science: What is Skewness and Why is it Important?

Data Science Skill #3: Programming knowledge

Machine Learning has seen a great jump only because of the boost in computing power. Programming provides us a way to communicate with machines. Do you need to become the best in programming? Not at all. But you will definitely need to be comfortable with it.

First of all, choose the programming language of your choice. Python, R, or Julia are to name a few and each has its own set of Pros and Cons. Python is a general-purpose programming language having multiple data science libraries along with rapid prototyping whereas R is a language for statistical analysis and visualization. Julia offers the best of both worlds and is faster. If you are confused about which language to choose, I have compiled a resourceful article for you –

• 5 Popular Data Science Languages – Which One Should you Choose for your Career?

Honestly, I have found Python to be a lot easier to perform machine learning tasks, due to the availability of libraries and high support for deep learning.

Data Science Skill #4: Data Manipulation and Analysis

Data manipulation or wrangling is the step in which you clean the data and transform it into a format that can be analyzed better in the next stages. Let’s take the example of packing your luggage. What will happen if you throw all your clothes into your bag? You will save a few minutes but it’s not an efficient way to do it and your clothes will also get spoiled. Instead, you can spend a few minutes ironing and putting them in stacks. It will be much more efficient and your clothes will remain in good condition.

Similarly, data manipulation and wrangling make take up a lot of time but ultimately help you in taking better data-driven decisions. Some of the data manipulation and wrangling generally applied is – missing value imputation, outlier treatment, correcting data types, scaling, and transformation.

Data Analysis is the step where you understand all about the data and take its “feel”. This is usually the step where you learn a lot about the data. For example, what’re the average sales per week, Which products are bought the most and so on.

Data Science Skill #5: Data Visualization

To be honest, this is one of the most fun parts of machine learning, Data Visualization is more like an art than a hard-wired step. There is no “One size fits all” approach here. A Data Visualization expert knows how to build a story out of the visualizations.

To start with you must be familiar with plots like Histogram, Bar charts, pie charts, and then move on to advanced charts like waterfall charts, thermometer charts, etc. These plots come in very handy during the stage of exploratory data analysis. The univariate and bivariate analyses become much easier to understand using colorful charts.

Data Science Skill #6: Machine Learning

Finally! The skills that give inner satisfaction!

For a data scientist, machine learning is the core skill to have. Machine learning is used to build predictive models. For example, you want to predict the number of customers you will have in the next month by looking at the past month’s data, you will need to use machine learning algorithms.

Data Science Skill #7: Deep Learning

Motivated by smart assistants or the cool self-driven car segment or perhaps the funny videos created using deepfakes? All has been possible due to Deep Learning. It is a high growth vertical in the field of Artificial Intelligence thanks to advancements in data storage capabilities and computational advancement.

To excel in this field, you must be well versed in programming (preferably with Python) and have a good grip on linear algebra and mathematics. To start off, you can start building basic models and then jump to advanced models like CNN, RNN, and more.

Data Science Skill #8: Big Data

We are generating data at a rate of 2.5 Quintillions per day! Due to the rise of the internet, social media networks, IoT there has been a sudden boom in the rate of data we are generating. This data is high in volume, velocity, and veracity which form the 3V’s of Big Data.

Organizations have been overwhelmed with such a large amount of data and they are trying to tackle this data by rapidly adopting Big Data Technology so that this data can be stored properly and efficiently and used when needed.

Data Science Skill #9: Software Engineering

To write a high and good quality code that won’t cause havoc during the production stage, it is necessary to know the basics of some of the software engineering subjects like – basic lifecycle of software development projects, data types, compilers, time-space complexity, etc.

Data Science Skill #10: Model Deployment

 An insurance company has initiated a data science project which uses Vehicle images from accidents to assess the extent of the damage. The data science team works day and night to develop a model that has a near-perfect F1 score. After months of hard work, they have the model ready and the stakeholders love its performance but what after that?

Remember that the end-user, in this case, are the insurance agents and this model needs to be used by multiple people at the same time who are NOT data scientists. Therefore they’ll not be running a Jupyter or Colab notebook on GPUs. This is where you need a complete process of model deployment.

Data Science Skill #11: Communication Skills

“Good communication is just as stimulating as black coffee, and just as hard to sleep after.” – Anne Morrow Lindbergh

Data Science projects are more of a treasure hunting job, the treasure being the insights you fetch from the data. The question is what is the price of the treasure? Well, that is decided by your stakeholders. The only way to get a good price is to be able to communicate how insightful the results and how can this treasure help them in improving the profits and organization.

Furthermore, the quality of a great data scientist is to formulate the problem statement. At the start of the project, the stakeholders tell their requirements to the data scientist, and then the latter formulate a problem statement. For example, the stakeholder needs to improve the content recommendation of their OTT platform so that the retention time increases. This is a very vague description, it’s the job of the data scientist to communicate the right problem statement.

Data Science Skill #12: Storytelling Skills

Imagine watching a cricket match stats, you are shown with the runs scored on each bowl in the form of a table. Do you think you will get any important information from this? What if you are you are shown a bar chart of runs scored in each over? Seems better. Right? It is not in human nature to understand blocks unless you make them interactive.

Data Science Skill #13: Structured Thinking

Let us say that you want to become a data scientist – you will break this large goal into multiple parts like training, preparing your resume, applying for a job likewise the ability to break down a problem into multiple parts so as to efficiently solve it is Structured thinking.

Data Science Skill #14: Curiosity

Why did this happen? How did this happen? If I tweak this, will it affect the overall results? Continuously asking questions is one of the most crucial soft skills of a data scientist. If you are dull, you may follow all the steps of the machine learning project lifecycle but you won’t be able to reach the end goal and justify your result.

Data Science is still evolving and it let me tell you the most important thing – Learning never stops in this field. You master the tool one day and it gets run over by an advanced tool the next day. A data scientist needs to be curious and always learning.

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