In a world where data is generated every second, rewarding careers have emerged. A data scientist is a sort-after career today. This is because industries are trying to make the best data-driven decisions to maximize their profits in the most efficient way possible without compromising on quality

Data science is a future-proof career to some extent if you think about it, a world that is interconnected through technology, loads of data is generated every second. Companies are at the forefront of using this data to make sense of customer behavior, improve the quality of their products, and generally improve their operations and sales.

Table of content

a. What is Data science?
b. What does a Data scientist do?
c. What skills are needed to be a data scientist?
d. What tools are used by a data scientist?
e. Data scientist project Life cycle
f. Difference between a data scientist and other data fields
g. Conclusion

Who is a Data Scientist?

Data science is a multi-disciplinary field of study that involves big data. Companies are increasingly incorporating it into their operations to improve efficiency, retain quality, and improve their fortunes. It involves creating new innovative ways of modeling and understanding the unknown from raw data.
Data science helps in making data-driven decisions by studying massive amounts of data to extract meaningful insights.

What does a Data scientist do?

A Data scientist helps bring to understanding what volumes of data mean and how the business can use this information to their advantage in decision making. To achieve this, a data scientist works with various stakeholders in the business.

A data scientist extracts data from various sources, stores this data, prepares the data, and analyzes it using machine learning models to give meaningful insights.

Skills Required to be a Data Scientist

Data scientists are required to be very curious first and foremost. This is because they deal with massive amounts of data. This allows them to create models that can answer the right questions as far as the data is concerned. Other skills needed to be a data scientist are;

1. Communication skills,
2. Mathematics and Statistics,
3. Visualization,
4. Deep learning,
5. Data Wrangling, and
6. Data warehousing

Tools Used by a Data Scientist

Data scientists are mostly required to understand Python and R programming languages. Python helps create lightweight algorithms used in data manipulation. In-built frameworks like Matplotlib, Pandas, Seaborn, Keras, Scikit-learn, Tensorflow, Pytorch, and Beautifulsoup make it easy to extract insights from data.
Other tools used by Data scientists are visualization tools like PowerBI, Tableau, and Excel. In addition, some data scientists may be involved in data warehousing projects where structured and unstructured data is stored. This therefore requires the data scientist to understand SQL and NoSQL.

Data Science Project Life cycle

Using the skills highlighted and the tools available, a data scientist can carry out various projects. These projects are expected to follow certain guidelines to make meaningful insights into the data. These steps are;

1. Business Operation understanding – for a project to make sense to stakeholders, the scientist works with all the business stakeholders to understand the operations of the business. These help in understanding where Data will come from as well.

2. Data mining – involves collecting data from various sources such as the companies’ databases, and web scraping from websites.

3. Data cleansing – involves eliminating inconsistencies in data to ensure accuracy in the results

4. Data exploration – Data is analyzed at this stage to ensure it answers the business questions intended for the project.

5. Predictive modeling – involves training machine learning models, performance evaluation, and using them to make predictions.

6. Data visualization – Findings are then communicated to stakeholders.

Difference Between Data Science and other data fields

Data science shares similarities with various data fields. Let us break down these fields and the various roles they serve.

1. _Data Analyst _ Involved in extracting and reporting insights from data for businesses to make informed decisions.
2. Data Engineer Is involved in designing, building, and managing data infrastructure, creating data pipelines, and ensuring optimal performance.
3. Data Architect They are involved in designing the overall structure and organizing data within a business. Data model creation and definition of data standards are among some of their roles.

Conclusion

Data Science is a critical aspect of business operations in these times where data is the new "oil". Data scientists can work in various industries like medicine, finance, and manufacturing. This therefore means that demand for data science professionals can only grow with time.