arbiyeIntroduction*
Data is powerful information that is used to make decisions in boardrooms, or even by yourself. Data analytics is using the information gathered in the data, using the correct tools and techniques to identify and predict trends and patterns, which you will determine your next course of action.
You are addressing specific questions and challenges that your organization faces to have a better outcome in the future. This can be by checking what an how our consumers act, and the type of competition in the market. Data analytics helps organization make smarter decisions to succeed and be better.
Here, we are going to learn about data analytic, what tools to use and how to be good at it.
What is Data Analytics?
Data analytics is the process of data collection, management and tools used. Its primary objective is to come up with solutions by studying what we know and have to be better at what we do.
Understanding Data Analytics
This is the process of examining data, understanding it and extracting important information from it to make decisions that will propel your business further.
There are different types of Data analytics
a. Descriptive Analytics- looking at what happened
b. Diagnostic Analytics- why it is happening
c. Predictive Analytics- uses data to determine what will happen
d. Prescriptive Analytics- solutions to what needs to be done
Steps of Data Analysis
These are the simple steps you need to take when doing an analytic project:
- Define your problem: note what your are trying to solve
- Collection of data: gathering information from different sources, e.g. internet, business records, or customer feedback.
- Organizing data: cleaning and fixing mistakes in the data and making it easier to look at.
- Analyzing the data: using programs and tools like SAS to find patterns or trends in the data.
- Visualizing the findings: understanding the data and presenting your findings to plan the next step.
Data Analytics Tools and Techniques
As a data analytic professional or newbie, there are tools and techniques that are a must have. You are to learn how to use them to make your work easier. Each tool is to make has its purpose.
For storing data we have:
SQL databases like MySQL
NoSQL databases like MongoDB
For analyzing data, we use:
- Python, SAS and R
For visualization;
- Tableau, Power BI and Excel turn complex data into comprehensible graphs and charts
We use Google Analytics to track user behaviour and traffic on the web.
Once you have the data, there are a couple of techniques employed to extract useful information. They include;
Machine Learning - to enhance your predictive analytical skills.
Regression Analysis - understand relationship between variables.
Classification Analysis - for assigning data to their defined classes.
Time Series Analysis - forecasting and trend analysis.
Career in Data Analytics
If you are looking into a career as a data analyst, you have to be prepared and learn several skills and know how to use the tools named above. It being a fast moving industry, there are many opportunities for you to venture into different fields.
These technical skills can be taught through courses and training an include:
Data Preparation - for analysis
Data Visualization
Knowledge of Statistical Programming Languages - like Python, R, SQL.
Machine Learning and AI - how to incorporate them into your business and how AI can be trained to offer solutions.
There are soft skills you should also have to make it easier for you when it comes to decision making.
Critical thinking when handling problems from different perspectives.
Business acumen - knowledge of the industry and business problems encountered.
Intellectual curiosity - outside-of-the-box thinking to propel your business further.
In Conclusion
Data Analytics can be applied in a myriad of industries such as marketing, healthcare, tech, or/and finance. The right tools and skills can help an organization produce results that are applicable easily. Hope this breakdown helps you know what you are getting into, all the best in your new career.
