Data analyst is one of the smartest, most lucrative, and most demandable jobs in the 21st century. The average annual salary for a data analyst is around $65,000-$75,000. You can even work from home as a freelancer. In today’s modern world data is everywhere. Data is considered to be the most precious asset in the modern world. It represents our behaviour, social structure, and our lives. If you love analytical research, this can be a new opportunity in your list of career options.

Data analytics are termed as the process of extracting meaningful insights from raw data, such as hidden patterns and customer preferences. This is done by studying & analysing the procured data. Data analysts have different job descriptions according to their required skill level and field of interest. Data is collected from many sources like database backups, flat files, APIs, etc. Sourcing and aggregating data is the integral part of data collecting jobs. Cleaning, standardizing and normalizing data refers to data cleaning jobs. Setting up the reports & visualizing them for better analysis is a part of data visualization jobs. Every job description requires different levels of skill setups and interests. Considering one of these  will make your job seeking easier. 

If you think you need to be a programming geek or super smart mathematician to do a data analytics job, then you are completely wrong. It’s a myth considering that you need to be super smart in problem-solving in this sector. In fact, basic analytical skills will help you go a long way. If you dedicate 4 hours every day, then in less than 6 months you will be acing data analysis. You need to emphasize on the basics to understand the concept. Eventually in your actual job, you will get some hands-on experience.

The first things you need to know are statistics & mathematics. For statistics, you need to know:

  1. Mean, Median and Mode 
  2. Deviation & dispersion mean.
  3. How to draw a relation between data using correlation, covariance and an index number.

For probability, you need to understand how the distribution works and the usages of Bayes theorem.  There are many books for all of these. But the preferred one is Statistics for Business & Economics by David R. Anderson; Dennis J. Sweeney; Thomas A. Williams; James J. Cochran.

For mathematics,  a deeper understanding about integration, optimization and gradient descent is required. A course from khan academy can be your guide to finish this topic.

You can learn any programming language related to data science. However, Python and R programming languages are highly recommended. You don’t need to become a programmer or a master of it, just learning the basics of it will be enough. You need to learn Numpy and Panda programming for handling mathematical functions and calculations. For Python and Numpy, w3school will be a great help for you. You may also need to learn matplotlib or seaborn. After learning all of these tools, you need to have a better understanding about time series complexity of algorithms and storing data in the database.

Deeper knowledge and skill set of using MS Excel and graphs are required for mutating Raw data. It’s not necessary that you have to do some courses on MS Excel. You can easily learn more about this tool by exploring the software. However, The Microsoft Excel Tutorial is a great source for learning the basics of this tool. Graph theory is another branch of mathematics. So, never expect to finish the entire graph concept. It is neither possible nor required. Just try to cover this book, Play with Graphs by Amit Agarwal. This is more than enough for what you need.

By completing all of these, you will be able to do some entry level data analytics jobs. You can even do some case studies. Freelancing sites like Upwork, Fiverr, etc are useful for getting freelance jobs. To build a promising career, you have to take your knowledge to the next level. You will have to learn machine learning, deep learning and Linux & version controlling.

Google’s free course on machine learning is the best resource for getting started with machine learning. You need to be able to build neural networks & TensorFlow. You will need to know how to use the Tensorflow hub and tensor board. Apart from learning statistics, mathematics and programming, you need to know how to manage and collaborate with others on the software you are creating. 

You can handle big data & external data using python, but some external tools and libraries have been developed for reducing the hassle for the analysts. However, these are only the external tools and libraries. You have to learn the programming before using these libraries. You can learn tableau or hadoop. Learning these tools and the level of expertise differs from time to time. These libraries aren’t essential or required to learn to become a data analyst. These are optional. Learning these will simply help you to do your jobs faster and more accurately. 

Considering this modern data driven era, the demand for this profession is rising day by day. Though it might seem a bit complicated and lengthy process, it’s a very interesting & lucrative job for those who love problem-solving. So, if you are that person, why not make a head-start into the data science world?