Data can be referred to as raw and unprocessed information. They can either be in form of numbers, text, audio, images, videos etc. Everyday we willingly share our data on social media, eager and excited to share our stories with the world. According to a study by Seagate UK;

 “By 2025, there will be 175 zettabytes of data in the global datasphere”.

Data will continue to be generated daily and organisations need someone to assist them in leveraging this data for their business. The process of drawing useful insights from data is referred to as data analysis.

Data analysis has been adopted in different sectors, be it agriculture, public health, banking, development and in every other sector you can think of. Hence the ever-growing need for data analysts; organisations will always need someone to help make sense of the data generated in order to make meaningful and insightful decisions that will positively affect the business.

Data-Lead Africa is set to make you the “man for the job”! How cool is that? We have a programme specifically meant to enable organisations leverage their data and also individuals who will like to work in the data analysis field. The Data Analytics Bootcamp is a 3 months intensive program designed to train and equip individuals in the Data Analytics space who are seeking to accelerate their career with job ready skills in Data Analytics.

The programme provides students with the tools and technique for transforming data generated within an organisation. In three months, you will be taken through a journey from the basics of data analysis as well as tools used in the process of analyzing data. Data analysis is a very broad field and can be broken down into the following steps:

1. Data Collection

Data is raw and unprocessed information. When one is armed with the right information, there is no limit to what one can achieve. In order to make use of this data and leverage the information gotten from it, it has to be gathered which is the first step. This process is known as data collection.

Data collection is the process of gathering, measuring, and analyzing accurate data from a variety of relevant sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. (Source : https://www.simplilearn.com/what-is-data-collection-article).

In order to collect data, there are certain softwares that can be used to make data collection easier and faster for analysis. Some of these softwares includes; Kobocollect, ODK (Open Data Kit), Teamscope, REDcap, Survey CTO amongst others.

We will be training you on how to use two of these tools in the Applied Data Analytics Bootcamp which are Kobocollect and ODK.

2. Quantitative Data Analysis

Data can either be numeric or non-numeric. In data analysis, numeric data can also be known as quantitative data. The data collected during the data collection stage will need to be analysed in order to make meaningful decisions.

Quantitative data analysis is all about analysing numeric data (which includes categorical and numerical data) using various statistical techniques. In order to analyse quantitative data, there are certain softwares that can be used. Some of these softwares are SPSS, MS Excel, STATA and R for Big data analysis. All these tools will be taught in the bootcamp.

3. Qualitative Data Analysis

At the beginning of this article, recall that data can be in form of text, audio, videos, images; all these are qualitative data. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

 Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents. Qualitative data is non-numerical and unstructured. Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth.

To analyse qualitative data, the usage of softwares like Atlas.ti, Deedos, NVIVO, QDA miner amongst others. As a participant of the bootcamp, you will learn these softwares in the bootcamp.

4. Data Visualization

After analyzing the data gathered and generated, you will need to display the insights gotten from the data in interactive charts and visuals. You will agree with me that as humans, we are more visual as we like to see images and videos which helps us understand better rather than reading texts.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data (Source: www.tableau.com)

For data visualization, we have tools like Tableau, Power BI, MS Excel, Google Charts, Tableau, Grafana, Chartist. js, FusionCharts, Datawrapper, Infogram, ChartBlocks, and D3. Js

Participants of the bootcamp get to learn MS Excel and Power BI.

As a data analyst, your primary focus shouldn’t be about learning the tools as tools keep changing and evolving.

Interested in being part of the next cohort of the bootcamp? Join the waiting list here.