Must-Have Data Science Skills - Part2

Data Science Skill #1: Data Manipulation and Analysis

Do you know what separates a great machine learning project from the rest? Data Wrangling and Analysis. Although these are two different steps I have included it at the same point because of the sequence.

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 Analysis is typically done in Excel, SQL, Pandas in Python and is the most important task of an analytics professional whereas in machine learning data analysis is a step in the whole process.

Data Science Skill #2: 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.

If you are wondering which tools you use during this step then don’t worry. Every language discussed above offers a great set of libraries for advanced charts. If you want to take a step ahead and impress your seniors then Tableau is the way to go. It offers a smooth interface with drag-and-drop functionality.

Data Science Skill #3: 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.

You can start with a simple linear and logistic regression model and then move ahead to advanced ensemble models like Random Forest, XGBoost, CatBoost, and so on. It’s a good thing to know the code for these algorithms (which just takes 2-3 lines) but what’s most important is to know how they work. The best way to learn machine learning is by practicing problem statements.

Even though one is equipped with all the technical skills for data science, some general skills which put one to leverage. These competencies can help in succeeding in the field of data analytics realm.

1. Machine learning

ML involves teaching the computer systems which are equipped with data and algorithms to predict without programming. It is an amalgamation of data science, math, and software engineering. It could be used in marketing, customer service chatbots, product development, and social media.

2. Statistics

The analysis of large and massive data sets can be a humongous task. It can be both complicated and multifaceted. As there can be both structured and unstructured quantitative and qualitative data, a data analyst should grasp basic statistics. The knowledge of linear regression, classification, and resampling can also come in handy for the analysis aspect.

3. Business intelligence

With knowledge of data analytics and advanced technical skills, strong business acumen can bring significant impact in the organization. The understanding of organizational constraints and challenges can help form insights, recommendations, and predictions from the analyzed data. It will help form informed, data-backed arguments for suggestions and improvement in organizations.

4. Communication

A proficiency in storytelling, forming a thread with the findings, and presenting the necklace of new insights is an important skill. As data is becoming paramount in decision making across the domains, an analyst should be able to translate the complex information and analysis in a simple, well-deduced manner for the understanding of the audience.

5. Critical thinking and problem solving

Big data and its analysis pins on the ability to form a hypothesis, make inferences, find the relationship between different attributes, to conduct experiments for finding the solution of the problem statement. Analysts are supposed to think critically, creatively, and conclusively and apply their human judgment with technical analysis to solve the impending problem.