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Learning Data Analytics

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A good data analyst creates order in the chaos of data that every organization collects. In addition, the data analyst ensures that other people in the company can use the data to do their work more effectively.

Many large organizations are struggling with the problem that a lot of data is available, but that the data isn't structured. As a result, data is lost and this is at the expense of productivity. Big data will make this problem worse. Analysts will therefore be needed more and more.

We explain how you can become a successful analyst in 10 steps and if data analyst remains relevant for the future.

1. Learn programming

As a good analyst you need to be able to program in such a way that the available data can be used as optimally as possible by the specialists. The most common languages ​​are ' Python '; and " R "; in combination with Ggplot2, Reshape2, Numpy, Pandas, and Scipy. If these terms are new you can immediately start studying. If you have the right program skills, you can go straight to step 2.

2. Freshen up your knowledge about statistics

Now that you can program, the question is what should be programmed then. The data that's available must lead to meaningful statistics. it's therefore important that your statistical insight is such that you can compile the correct input for your programming work.

An analyst uses descriptive and predictive statistics for this. If the terms 'standard normal', 'chi-squared'; and ' median ' being known is enough to repeat. In other cases, more extensive training is required.

3. Has your knowledge of mathematics not yet been deleted?

Data analysis brings discussions and questions back to numerical comparisons. Interpreting and analyzing data therefore requires mathematical knowledge. Are terms such as matrix manipulations, dot products, eigenvalues ​​and vectors no longer familiar to you? Then your math knowledge seems cleared. In any other case, freshening up is sufficient.

4. Learn the data to learn from yourself with algorithms

We are at the start of the era of artificial intelligence. This intelligence functions through algorithms. it's not up to the data analyst to be able to program algorithms, but knowledge of the most important algorithms is indispensable. After all, algorithms will increasingly be used for data analysis.

With knowledge of the correct algorithms you can apply data to validate, predict and organize.

5. What should you be able to do with the data itself?

Now that you have the knowledge to make programs that can properly process the data, have the statistical and mathematical skills " again " to interpret the data and can use the appropriate use of data with algorithms, it's time to get started with the data to go.

There are three types of data processing that you regularly encounter as an analyst. We put these below each other.

Organize data as an analyst

Data comes in unstructured. To properly organize the data, you must keep an overview as an analyst. What do you need the data for and how do you sort the, sometimes chaotic, data in a logical and consistent way?

To answer these questions you have in addition to the proverbial 'helicopter view'; capabilities also needed to manage various programs. Consider the application of SQL-based programs, Oracle or MongoDB.

Visualize data as an analyst

Analyzing data isn't a goal, but a means. So it's important to know why your future colleagues need the data they request. If you know this, you can give them the necessary information at a glance with the right visuals. Knowledge of programs such as: Ggplot, Matplotlib, Sea Born, and D3.js is then required.

Intuitively analyze data as an analyst

An infinite amount of data can be drawn from a set of data. As a good analyst you know where to look in order to find precisely the data that your colleagues are looking for. The instinct for being able to quickly distinguish the relevant data is called intuitive analysis. You only learn this skill through experience.

6. Be prepared as an analyst for your work

This sounds strange, but the analyst often works in a team with several specialists. Moreover, data is often supplied in multiple ways. That's why good preparation is always important.

What are the roles that are shared in your team and who plays which role? What information is needed and where can you find that information? Is it your job to find that information? How do you get your data, which systems are used for this?

All practical questions that you, as an analyst, must have filled in for a practical start.

7. Know your limits

As described above, as an analyst you can get infinitely many analyzes from one data set. By knowing what your environment needs the requested data for, you can limit the number of analyzes and thus maximize the quality of the relevant analyzes.

It's also important to know and discuss within the team the limits that are associated with your position. This prevents you from doing too much or too little work and this limitation also benefits your actual duties.

8. Use your creativity

Sometimes you have to let go of your normal analysis methods to still deliver the data that your colleagues need. Can you perhaps make intermediate steps in the data or be creative in some other way? Sometimes you dare to let go of the processes.

9. Dares to let go

Your colleagues are also there to exchange ideas with you and share their knowledge. Dare to use that knowledge. The same applies to your analysis. There will come a time when someone else will start using your analysis. Dare to let go!

10. Dare to invest in yourself

With this top ten we have shown you how you can assure yourself of a good job with a good income. Dare to invest in yourself and seize that opportunity.


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