Top 10 Recession-Proof Data Manipulation Skills to Land a MAANG Job

Data analysis is a challenging task, especially if you don't have data manipulation skills

In today's tech-driven world, data is rapidly expanding, and which become extremely essential to get the right data to be organized for analysis. Given the massive amount of data that is produced, it is one of the most debated topics in IT circles. Business users depend on data and information to make just about every business decision. Therefore, it is a crucial part to use raw data for analytics. Data analysis is a challenging task, especially if you don't have data manipulation skills. Data manipulation means organizing or arranging the kind of structured data that is read by computer programs so that it's easier to interpret. Performing this process effectively can improve the quality of your data and analysis. This article lists the top 10 recession-proof data manipulation skills to land a MAANG job.

• Top 10 Data Wrangling Tools that Data Science Students Should Learn in 2023
• A Beginner's Guide to Data Analysis in Python
• Top 10 Recession-Proof Data Science Skills to Master in 2023

Filtering

Filtering is a process of sorting data by specific criteria. It's an effective way to identify subsets of data from the larger dataset. The following example shows a dataset with monthly sales data from 2012-2015. Filtering is helpful if you want to see the total sales for the year only, or if you want to know how many months had positive growth.

Programming

You need to have an understanding of various programming languages, such as Perl, C/C++, and Java for a data science role. These programming languages help data scientists organize unstructured data sets. This is among the top recession-proof data manipulation skills for MAANG jobs.

Grouping

Grouping makes it easy for you to identify patterns in your data. For example, if you wanted to know how many people are in each age group (20-25, 26-30, etc.), then you can group that information by age group and then count the number of people in each one.

Statistics

Statistics is a crucial part of data science. Generally, statistics are used to describe insights pulled from big data and/or forecast and make predictions. Many core data analysis techniques such as regression, time series, and factor analysis fully rely on statistics.

SQL

SQL is among the most learned data science skills for 2023. This popular programming language helps to communicate with databases and extract data for application development, reporting, and analytics. It is a supreme skill for data science professionals and business leaders looking to conduct complex calculations and forecasts.

Adding Columns and Rows

Adding columns or rows to your data is a great way to make your work more efficient. For instance, if you were working with a table of data on different subjects and wanted to look at their answers in relation to each other, it would be more convenient for you (and the people you're sharing the data with) if you had both answers in one column.

Excel

Spreadsheet tools like Excel can perform data manipulations, data processing, and even visualizations. If you are running short of time to perform complex data analysis, you may want to consider investing your time in learning Excel. This is one of the top recession-proof data manipulation skills.

Pivoting

Pivoting data involves taking a table and turning it on its side to show a different perspective. For example, let's say you have a list of monthly income brackets and want to see the monthly income distribution for each bracket. You can do this by pivoting your table from column to column.

Changing Data Types

One thing that might be useful to know is how changing data types can affect your data analysis. Two different types of data are text and number. Text data is any kind of information that isn't numerical. For example, a person's name or the title of a book. Numeric data will always be numerically based and may only have numbers in them, such as 3.1, 4.9, and so on.

Unstructured Data

Data scientists should have an understanding of handling unstructured data that comes from different channels and sources. For example, if a data scientist is working on a project to help the marketing team provide insightful research, the professional should be skilled at handling social media as well. Some other data science skills required are knowledge of Machine Learning, Artificial intelligence, Deep learning, Probability, and Statistics.