Over the past couple of years, there has been a rising need for Data Scientists by businesses, and institutions ranging from retail, banking, health, transport, agriculture etc.
But .. what do data scientists do? what does it take to become one?
Here is an example to break this down
You operate a supermarket and the common activities happening here are supplies being received from your various supplies and the sale of these goods to customers. All these activities generate data which, if well utilized can become a great asset by your management to run your businesses successfully.
Therefore, Data Scientists are professionals responsible for getting the data generated from your business, organizing, cleaning, and exploring these data to extract insights or meaning which can then be used for decision-making
An example of the insight can be; by analyzing sales data and inventory levels, you can identify patterns and trends in product demand.
This information can help the supermarket optimize its inventory management. For example, if the data shows that a particular product has a higher demand on weekdays but has lower demand during weekends, the supermarket can adjust its restocking schedule to ensure the product is available when it's needed most. This can lead to reduced carrying costs and increased sales, ultimately improving the supermarket's profitability.
Data Scientists then use this data to build prediction systems/ML models that be used to predict sales in coming days, weeks, etc. for the supermarket to stock goods accordingly and thus minimize huge losses that can happen as a result of this.
Now, what does it take to a Data Scientist?
Here is RoadMap:
1. Math Skills: Basic math skills, including probability, basic statistics, and some calculus, are essential for understanding and working with data.
2. programming skill, the common data science programming language is Python and you start by getting the basics by either getting an online course, blogs, or YouTube depending on your learning preference
3.Data science packages, in most cases, you will be doing data cleaning and manipulation, and Python already contains these packages.
The commonly used data science packages are;
- Pandas for most data manipulation and exploration
- Numpy for numeric operations
- Matplotlib and Seaborn for visualization
- Sklearn which contains algorithms used for building ML models
The most important thing to accelerate your learning is to connect with people already in the field, learn from their experiences, and build on what you're learning.!
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