Getting started in data analysis

Getting started in data analysis

Want to begin solving with data but not sure where to start? In the upcoming blog posts, I will share some

Based on my experience, to be effective, these are some areas to focus on when getting started in data analysis

Getting started in data analysis

1. storage and access to data and efficiency of different data formats

To begin, you will need to know where to find your data, and the tools and skills required to access and manipulate different data formats. Common data formats are text files, spreadsheets like Excel and databases. You will want to know what each one offers as it can impact on your efficiency and productivity.

Python for data analysis
R for data analysis
SAS for data analysis

2. understand the tools available to describe, analyse, explore your data 

There are many different tools available for data analysis. Some tools like Python, R are open-source and free, while others like SAS, may cost some money. Some have a steep learning curve and may be less user-friendly while others may be user-friendly but have fewer features or are more time consuming when it comes to data analysis. You will want to know which tools best suits your needs.

tools for data analysis

3. awareness of different analytics techniques/algorithms and understand when to apply them

To make inferences, predictions from your data, you need to be aware of the different techniques, when to apply them and the level of accuracy. Some techniques like clustering, provide you insight into the different patterns within your data, while other techniques use past trends to forecast values like prices, sales volume etc. Understanding which technique should be applied will help you address your objectives

Return and Value of data analysis

4. commercial/business case assessment skills to articulate return and value of your analysis 

The hard number crunching is completed and you have your findings but it doesn’t end there. You now need to take your findings up a level and quantify how your analysis will benefit you or your organisation. In most places, this benefit is measured via revenue and profitability. Translating your findings into future revenue and profitability is key.

presentation techniques for data analysis

5. effective communication methods and tools to present your findings

If you are not the sole decision maker, then, most likely, you will need to communicate your findings and recommendations to an audience. This is one of the most important areas in any analytics project. Audiences need to be engaged and providing feedback. In some cases, their buy-in may be required to proceed with your recommendations. Failure to present well may result in all your effort going to waste.

Throughout my data analytics work, I’ve found these 5 areas to be important factors. Building knowledge in these areas will assist you in getting started in data analysis.

I would also note that there is no one size fits all solution. As you progress, you will find that different tools and skills can be used to achieve the same outcome.  Having a broad view of what’s available can help you find the right solution and increase your productivity.

In my next blog post, I will dwell more on the 1st area: storage and access to data.

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