Today’s data is priceless, probably worth its weight in gold for companies. But decoding all that data and figuring out what it means for the companies takes a lot of skill and know-how. That’s where a data scientist comes in! They are the specialists who manage and analyse data and turn those undecipherable data into valuable insights. So, in this article, we will discuss who is a data scientist and what programs he must know.
Who is a Data Scientist?
Companies constantly strive to collect a ton of data in this digital age. A data scientist knows how to take all that data and turn it into something useful. They can analyse structured and unstructured data using intelligent techniques and then use that information to help the business make wise decisions. They’re the pros at data analysis and deeply understand how it all works.
Data scientists design and implement algorithms and statistical models to analyse data and communicate their findings to non-technical stakeholders. They also work closely with other teams, such as engineers and product managers, to ensure business processes and products can incorporate data-driven insights.
What Programs Must a Data Scientist Know?
To be a successful data scientist, you must know many tools and technologies like the back of your hand. Here are a few of the essential programs that you can’t afford to skip:
Programming Languages:
Python, R, and SQL are all must-haves for a data scientist. Python is the coolest kid on the block and is used for everything from making machine learning models to creating web apps and data visualization tools.
R is another popular language perfect for statistical computing and data visualization. And remember SQL, which manages and retrieves data from databases.
Data Analysis and Visualization Tools:
Data analysis and visualization tools clean, transform and manipulate data to extract insights and knowledge. Additionally, data visualization tools help you to communicate these insights clearly and concisely.
Some popular tools include Excel, Tableau, Power BI, Python, and R. While Excel is suitable for Data Analysis, Tableau and Power BI allow you to create powerful visualizations and interactive dashboards. Programming languages like Python and R offer a range of libraries.
Machine Learning Tools:
As we know, artificial intelligence is slowly taking over our daily lives. It also is a significant threat to a lot of jobs. As the name suggests, machine learning is what artificial intelligence does. But it is a subset of artificial intelligence and not the whole. It involves training algorithms to learn patterns in data.
Data scientists must be proficient in machine learning tools like Scikit-Learn, TensorFlow, and Keras. Scikit-Learn is a Python library used for machine learning, while TensorFlow and Keras are deep learning libraries used to build neural networks.
Big Data Tools:
Some datasets are large and complex. You cannot process using traditional data processing tools. Therefore, you need proficiency to process such data. So, data scientists must have expertise handling big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka.
An open-source framework like Apache Hadoop is used for distributed storage and processing large datasets. At the same time, Apache Spark is a fast and general-purpose engine for processing big data. For creating data pipelines and streaming applications that are real-time, a distributed streaming platform Apache Kafka comes in handy.
Cloud Computing Platforms:
Cloud computing refers to the on-demand delivery of computing resources over the internet. Data scientists must be proficient in cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
AWS is Amazon’s cloud computing platform, while Microsoft and Google provide Microsoft Azure and GCP.
Conclusion
Today in this digital age, data scientists are critical for helping organizations manage, analyse, and derive insights from data. They must have a thorough knowledge of all the things we discussed above. Also, they must be proficient in various latest tools and technologies. Thus, data scientists can help organizations to make data-driven decisions that can boost business growth and make them successful in the long run.
