What is data Science ?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

Roadmaps are strategic plans that determine a goal or the desired outcome and feature the significant steps or milestones required to reach it.
Therefore this is a roadmap to becoming a great data scientist.

The Data Science Lifecycle

Data science’s lifecycle consists of five distinct stages, each with its own tasks:

1.Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.

2.Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.

3.Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.

4.Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.

5.Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.

Step 1: Query Language Like SQL

SQL (Structured Query Language) is a programming language used for managing and manipulating data stored in databases. It is a critical skill for any data scientist, as it allows you to retrieve, filter, and aggregate data from various sources.

Step 2: Programming Language Like R/ Python
After learning SQL, the next step in data science for beginners is learning a programming language like R or Python. R and Python are widely used in data science for data manipulation, visualization, and machine learning tasks.

To get started, you can choose one of the languages and begin learning the basics. This may include concepts such as variables, data types, loops, and functions.

Step 3: Visualization Tool Like PowerBI/Qliksense/QlikView/Tableau

Once you have a solid foundation in programming and data manipulation, the next step as an enroller of data science for beginners is to learn a visualization tool like PowerBI, QlikSense, QlikView, or Tableau. These tools allow you to create interactive and visually appealing charts, graphs, and dashboards to communicate your data insights.

To get started, you can choose one of these tools and begin learning the basics. This may include topics such as creating charts and graphs, building dashboards, and connecting to data sources.

Step 4: Basic Statistics for Machine Learning
After you have learned a programming language and visualization tool, the next step is to learn basic statistics for machine learning. Machine learning is a subfield of data science that involves using algorithms to learn from and make predictions on data. To get started, you should learn basic concepts such as probability, statistics, and linear regression.

Step 5: Machine Learning Algorithms
Once you have a solid foundation in basic statistics, the next step is to learn about machine learning algorithms. There are many different algorithms used in machine learning, each with their strengths and weaknesses. To get started, you should learn about common algorithms such as decision trees, linear regression, and k-means clustering.

Step 6: Practice and Implementation
The final step in learning data science as a beginner is to practice and implement what you have learned. It can involve working on projects and exercises to apply your skills, as well as participating in online communities and forums to learn from others and get feedback on your work. You can also consider joining a data science group or club, which can provide you with additional opportunities to learn and collaborate with others.

To practice and implement your skills, you can work on real-world data sets and use the tools and techniques you have learned to explore, visualize, and analyze the data. You can also try building your machine-learning models and testing them on different data sets. This can help you gain practical experience and build your portfolio, which can be useful for job applications or freelance work.

Conclusion
Hopefully, this article could provide insight into the world of data science. To become a data scientist, it is important to be familiar with the programming languages used most frequently in the industry and some major data-related concepts. You can start with a data science course for beginners to become data scientists.

Data science is a fast-growing industry, and we are interested in seeing where it goes in the coming years. Data scientists need a broad skill set covering all these phases and domain expertise in the industry they serve.