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.