Must know Data analysis skills

1: Data Cleaning and Preparation

Data cleaning and preparation accounts for around 80% of the work of data professionals. This makes it perhaps the key skill for anyone serious about getting a job in data.

Commonly, a data analyst will need to retrieve data from one or more sources and prepare the data so it is ready for numerical and categorical analysis. Data cleaning also involves handling missing and inconsistent data that may affect your analysis.

Data cleaning isn’t always considered “sexy”, but preparing data can actually be a lot of fun when treated as a problem-solving exercise. In any case, it’s where most data projects start, so it’s a key skill you’ll need if you’re going to become a data analyst.

2: Data Analysis and Exploration

It might sound funny to list “data analysis” in a list of required data analyst skills. But analysis itself is a specific skill that needs to be mastered.

At its core, data analysis means taking a business question or need and turning it into a data question. Then, you’ll need to transform and analyze data to extract an answer to that question.

Another form of data analysis is exploration. Data exploration is looking to find interesting trends or relationships in the data that could bring value to a business.

Exploration might be guided by an original business question, but it also might be relatively unguided. By looking to find patterns and blips in the data, you may stumble across an opportunity for the business to decrease costs or increase growth!

3: Statistical Knowledge

A strong foundation in probability and statistics is an important data analyst skill. This knowledge will help guide your analysis and exploration and help you understand the data that you’re working with.

Additionally, understanding stats will help you make sure your analysis is valid and will help you avoid common fallacies and logical errors.

The exact level of statistical knowledge required will vary depending on the demands of your particular role and the data you’re working with. For example, if your company relies on probabilistic analysis, you’ll need a much more rigorous understanding of those areas than you would otherwise.

4: Creating Data Visualizations

Data visualizations make trends and patterns in data easier to understand. Humans are visual creatures, and most people aren’t going to be able to get meaningful insight by looking at a giant spreadsheet of numbers. As a data analyst, you’ll need to be able to create plots and charts to help communicate your data and findings visually.

This means creating clean, visually compelling charts that will help others understand the data. It also means avoiding things that are either difficult to interpret (like pie charts) or can be misleading (like manipulating axis values).

Visualizations can also be an important part of data exploration. Sometimes there are things that you can see visually in the data that can hide when you just look at the numbers.

5: Creating Dashboards and/or Reports

As a data analyst, you’ll need to empower others within your organization to use data to make key decisions. By building dashboards and reports, you’ll be giving others access to important data by removing technical barriers.

This might take the form of a simple chart and table with date filters, all the way up to a large dashboard containing hundreds of data points that are interactive and update automatically.

Job requirements can vary a lot from position to position, but almost every data analyst job is going to involve producing reports on your findings and/or building dashboards to showcase them.

There are some most sought-after skills needed to ace in the field of data science. A few technical skills are foremost to push your career growth in this field.

Big data is the fuel of the economy now. More the analysis, more the information. That’s why data science and analytics are becoming a paramount weapon in the arsenal of company growth. There are around more than 20000 jb openings for the below-mentioned skills that the employers are seeking.

Technology skills

There are some primary skills which employers expect from the applicants. They are mostly programming languages and tech tools. Some of them are mentioned below.

1. SQL

Structured Query Language SQL was the most posted requirement in the job description of the data analyst. This language makes retrieving data possible and provides analysts to manipulate large amounts relational database information

2. Python

The second in-demand programming language is Python. A user-friendly language for beginners, it is a high-level, object-oriented language. It helps in the analysis of sophisticated data sets. Moreover, a majority of libraries for ML and data science have a Python interface. For data visualization, Python proves to be a really powerful tool.

3. Tableau

An analytical platform and one of the tools for data visualization, Tableau helps analysts to transform the information from data to actionable insights. It helps in the preparation of data for analysis and visually appealing presentable data for easy interpretation by non-tech users.

  1. R

A programming language, R provides objects, operators, and functions. It is equipped for data manipulation, calculation, and graphical representation. It can handle store and analyze data and simplify big data analysis. It can perform a wide range of statistical and graphical techniques and also helps in predictive analysis of real-time data.

5. Hadoop

Hadoop’s main functionality is the storage of all forms of big data, both structured and unstructured. It has modules like Pig & Hive for large-scale data analysis. The Apache Hadoop software processes large data sets across varied computer clusters with simple programming models. This software provides a massive storage framework.