First of all its very important to see the difference between Data analysis and data science

What does it take to be a Data analyst? Skills: Data warehousing, BI tools, Tableau, SQL Roles and responsibilities: Performance reporting, Data visualization And using existing questions to extract actionable data Goals: Derives meaningful insights from the dataset for better business decisions Salary: Median salary that you will be placed is around 5LPA. What does it take to be a Data Scientist? Skills: Statistical Modelling, Predictive Analytics, Machine learning Roles: Performing Hadoop-based analytics and Finding new questions to drive innovation Goals: predicts the future of the industry based on insights Salary: Median salary of a data scientist is 10 LPA. Well, it does not matter what you want to be, these skills we are going to discuss now will always be a basic requirement to work with data. Sharpening this will make you a master in the field and aid you to raise the ladder effectively

And those following units of your arsenal must be

Programming Languages

Python Anyone who is aspiring to enter this area of work makes this a top priority. Because the general purpose of this language is it naturally comes with a number of packages Focus on aiding programming AI. The technical landscape is becoming increasingly AI-based, it's in demand for every analyst to be efficient in Python.

R language Old but not obsolete, this language has a syntax and structure that is built to support analytics. Also, it has the potential to handle large sets of data. This is why many businesses still use this language and it should be one of the most essential skills to learn for a data analyst,

Database language

SQL This is what makes to communicate with databases and analyze data. It's one of the most proficient languages. Almost every tech firm in the world needs people who are proficient in this language. It is mainly used to store and manage data, relate multiple databases or sometimes build new database structures.

NoSQL True to its name, This can also structure data in any way and there is no standard NoSQL framework. Data analysts should build their knowledge in these databases. Frameworks like MongoDB will be the best starting point for learning NoSQL.

Visualizing and Cleaning Data

Using these languages to process and drive the conclusive data and draw insights. It's essential to explain those insights to the stakeholders who may not have any clue about data analysis. This is where you use visualization to present the key findings with the help of graphics, pie charts, or other illustrations.

Data Cleaning 80% time a data scientist's job is to clean and verify data. Want to know why? because of the fact in ML. Better always beats fancier stuff, when it comes to algorithms, With a clean dataset, even simple algorithms can produce better information. Obviously, It's not a surprise that companies need a person who has the skill to refine datasets

Along with these mentioned skills Mathematical abilities and being handy with Microsoft Excel will also be an added advantage to aid your career path. To know more about shaping your future career