Data Science is a domain that is showing constant advancements over the last few years. In 2019, Data Science was also termed to be the sexiest job of the year. Upskilling in this domain can help you advance in your career and reach greater heights. This Data Science Tutorial has been designed to help you understand the basics and gain a foundational understanding of the concepts. Before we move on with this Data Science Tutorial, let us first learn more about what data science essentially means.
What is Data Science?
A field of study that aims to make use of a scientific approach to extract meaning, and to derive insights from data, Data Science can be defined as a study of data. Data Science studies where data comes from, what this data represents, and how the available data can be transformed to derive valuable inputs that will further help the business create strategies for use. The amount of data available to us has grown exponentially over the past decade. Organizing and utilizing this data can help a business derive meaningful insights for business use, research, creating plans and so much more.
Why Data Science?
In the past, we used to work with small sets of structured data. Today, we have a large mine of unstructured data and semi-structured data that is available to us from a variety of sources. When we talk about traditional business intelligence tools, they are sure to fall short while processing the large mine of unstructured data. Thus, Data science comes into the picture with a pool of advanced tools that can help us work on a large volume of data. Data comes from a vast pool of sources such as financial logs, multimedia files, sensors, marketing forms, text files.
Data Science is also used in predictive analytics, be it weather forecasting, collecting data from satellites, radars, ships or aircraft. These insights help predict natural calamities with higher precision. Thus, helping us take the required measures at the right time and reducing the damage.
Another advantage of data science is product recommendations. These recommendations have never been as precise as they are today. Traditionally, insights were drawn from purchase history and other demographic data. Today, a vast volume of data can be utilised to train models to perform effectively and show us more accurate recommendations.
It also helps in effective decision making. One such example would be self-driving cars. These intelligent cars collect real-time data with the help of sensors and create a visual map of the surroundings. This data combined with machine learning algorithms helps the car in taking decisions such as when to turn, when to stop, when to speed, etc.
Job Roles in Data Science
There are a number of job roles under the umbrella of data science that you can choose from. A few of these job roles are listed below. Each of these roles has a different set of roles and responsibilities and requires a different skill set, although the skills are overlapping in a few of the job roles mentioned below.
- Data Analyst
- Database Administrator
- Data Architect
- Business Analyst
- Data Engineers
- Data and Analytics Manager
- Machine Learning Engineer
- Statistician
- Data Scientist
Tools for Data Science:
This brings us to the last subtopic in our data science tutorial for beginners. Did you know that Data Science comes with several tools that are helpful for a data scientist during his projects? There is a wide range of tools available to a data scientist. These tools are divided into four categories, namely, data storage, exploratory data analysis, data modelling, and data visualization. Let us now discuss each of these categories and take a look at the tools that are available under each category.
- Data Storage: As the name suggests, these tools help you store a large amount of data. A few data storage tools are:
- Apache Spark
- Hadoop
- Microsoft HD Insights
- Exploratory Data analysis: EDA, or Exploratory Data Analysis is an approach that is used to analyze a large amount of unstructured data. A few EDA tools are:
- Informatica
- Python
- SAS
- MATLAB
- Data modelling: These tools come with inbuilt machine learning models. This makes it easy for us, as all you are required to do is to pass on the processed data to train your model. Some tools under data modelling are:
- H20.ai
- DataRobot
- BigML
- TensorFlow
- Scikit Learn
- Data Visualization: Once we are done with data processing, the next step is to visualise this data and find out any hidden patterns or insights. These insights can be used to build reports. A few of the data visualisation tools are:
- Matplotlib
- Tableau
- Seaborn
Conclusion:
This brings us to the end of the data science tutorial. We hope that you have gained a basic understanding of what data science means, why it is important, a few of the common data science tools and a few job roles that you can work towards after you learn more comprehensively about the concepts.
