So in the field of Data analysis, there are vital 3 stages in the career ladder you can grow. These are
1- Data Visualization/Business Intelligence
2 -Exploratory Data Analysis
3- Predictive Data Analysis.

There is no limit to the areas you can become an expert. You could choose to learn and gain mastery in any of these three areas or even all of them.

This may just require you to dedicate time and effort. The more of skills you have the more money for you 🤓

So for some of us coming from the financial sector (Banking, insurance, fintech, accounting), these 3 stages will be key to your career growth in Data Analytics.

While you kicks-tart your career in Data Analytics you could start with the first stage -Data Visualization/Business Intelligence

I do advise newbies to start with Data Visualization reason being that it has low a barrier of entry, a faster time of learning the technology, and it's also easy to get a job with your skill.

To further take your career to the next level, then you can now proceed to the second stage which is learning Exploratory Data Analysis and then you further to the 3rd stage which is the Predictive side of data analytics

To start with Data visualization, you will need to learn data visualization tools and technology.

The technology you need to learn here are;

• Microsoft Power BI or Tableau - For data visualization
• SQL - You will need a working knowledge of SQL to write queries to connect or fetch data from a database.

The reason is that when you join a medium to large-scale organization, the data you need to work with may not be available on your Excel sheets in your local computers anymore but on cloud databases or any other online data sources.

For visualization tools, I strongly recommend Power BI because it's leading in the Industry at the moment compared to Tableau which is not free.

You can download and Install the Power BI Desktop here 👉🏻https://powerbi.microsoft.com/en-us/downloads/ if you don't have it.

Resources: If you are the type that like learning through videos, this comprehensive starter course on Power Bi on Udemy is a great start -
https://www.udemy.com/course/data-analytics-essentials-with-power-bi/

For a simple project to practice with as a starter, use this step-by-step guide project. Its a supermarket sales report analytics - https://www.xelplus.com/power-bi-get-started

To work with SQL, you can install MySQL Workbench to write your SQL. You can download it here - https://dev.mysql.com/downloads/workbench/

For fundamentals and an indept understanding of SQL then Mode Analytics platform is your go-to place. It's a free platform. here is the link https://mode.com/sql-tutorial/introduction-to-sql/

To further step up your career in Data Analytics as a Data Visualization expert, you can then transition into Exploratory data analysis also known as EDA

EDA is basically understanding the data sets by summarizing their main characteristics often plotting them visually.

In EDA you will learn how to clean and extract a large amount of data from different sources like Twitter, Facebook.

The thing is that most of the data you will need to perform analytics are always in a messy form.

The data can be mixed with text, video, and images all together. You may need to clean them and extract what you want.

You can use EDA to identify inconsistency in data, discover missing values, anomalies, or outliers, and also know what key metrics to look out for in data.

Aside SQL, the two main technology to learn for EDA is Python and R programming.

Python is more widely used in the business analytics/financial space than R. I do recommend learning python.

Apache Spark is also a tool used too but for big data analysis.

EDA is very important especially when we arrive at modeling the data to apply Machine learning.

Resources - Datacamp is one of the biggest platform for this - Here is the link - https://www.datacamp.com/courses/introduction-to-data-science-in-python

To further deepen your expertise in Data Analytics, you could then proceed to the 3rd stage which is the Predictive side of analytics.

This one is advanced analytics used in predicting an unknown future.
The technology you will need to learn here is Python and some machine learning technologies such as Keras- its a python library for Machine learning, and Tensorflow).

Python is also the technology you will learn here for Machine learning. You also need a working knowledge of cloud technology like Azure

Predictive Data Analytics uses many techniques like data mining, machine learning, AI.

Some of its use cases in the financial sector are

• Fraud detection: You will be able to develop a Machine learning Model that can detect fraudulent financial transactions both done offline and online, identity thefts, and false insurance claims.

-Risk management -You can apply your skill to predict the best portfolio to maximize returns in the capital market, price model, and probabilities, risk assessment

Customer acquisition & retention:
You can apply your knowledge of predictive analytics to help in the process of optimized targeting, making it easier for financial institutions to instantly identify the high-value customer segments most likely to respond.

Knowing customer buying habits:
With predictive analytics, banks can rapidly segregate various customer segments and replace them with highly relevant, individualized messages tailored to each customer’s profile, resulting in a higher response rate.

Credit Scoring: Banks could use predictive analytics to calculate makeshift “credit scores” for people that don’t have a credit history based on behavioral traits such as social media posts and spending habits.

Communities to follow - Data Science Nigeria is a great community to get up to date info about Data Science , Analytics. Follow them on LinkedIn and twitter.

Its an open community to learn most stuff for free. https://www.datasciencenigeria.org/