Data Science is a field of study involving statistical tools and techniques to extract meaningful insights from data. Data science has become a major part of the modern-day business world, as it helps organizations make informed decisions based on logic and reason rather than intuition alone.
The need for data science has become increasingly important in today's world due to the vast amount of data being generated by businesses, organizations, and individuals. Data science provides the tools and techniques to extract meaningful insights from this data, enabling informed decision-making and has become essential for businesses to gain a competitive edge and improve their operations. It also plays a crucial role in addressing some of the world's most pressing challenges, such as healthcare, climate change, and social inequality. In short, the need for data science is vital in today's data-driven world to unlock the potential of data and make informed decisions.
It is the combination of statistics, mathematics, programming, and problem-solving; capturing data in ingenious ways; the ability to look at things differently; and the activity of cleansing, preparing, and aligning data.
Data science roadmap is a visual representation of a strategic plan designed to help learn about and succeed in the field of data science.
The steps
Learning data science as a beginner involves learning the necessary tools and technologies, understanding the underlying concepts, and practicing and implementing what you have learned. With persistence and dedication, you can build a strong foundation in data science and become proficient in the field. Below is a step-by-step data science roadmap for beginners to help you get started on your pursuit.
*Step 1: Learn Query Language Like SQL *
If you are a beginner in data science, a good place to start is by learning a 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.
Beginners can find many resources for learning SQL, including online courses, tutorials, and textbooks. You can also practice your skills by working on SQL exercises and projects. Once you have a solid foundation in SQL, you can move on to the next step.
Step 2: Programming Language Like R/ Python
After learning SQL, the next step in data science for beginners is learning a programming language like Python, SQL, Scala, Java, or R. 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. There are many resources available for learning R or Python, including online courses and tutorials available on the best websites to learn data science. As you progress, you can delve into more advanced topics and build your skills. Common data structures (e.g., dictionaries, data types, lists, sets, tuples), searching and sorting algorithms, logic, control flow, writing functions, object-oriented programming, and how to work with external libraries.
Additionally, aspiring data scientists should be familiar with using Git and GitHub-related elements such as terminals and version control. There are many resources available to learn Git and GitHub. For example, check out a Git tutorial here, or take Git and GitHub training here.
Step 3: Visualization Tool Like PowerBI/QlikView/Tableau etc.
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, 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. Many resources are available for learning visualization tools, including online courses, tutorials, and documentation. As you progress, you can delve into more advanced features and techniques.
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.
Many resources are available for learning basic statistics of machine learning. These include data science online courses, tutorials, and textbooks. As you progress, you can delve into more advanced topics and build your skills in machine learning.
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.
For beginners, many resources are available for learning machine learning algorithms, including online courses, tutorials, and textbooks. As you progress, you can delve into more advanced algorithms and build your skills in machine learning.
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.
Is Data Science a Good Career Option?
Data science is a rapidly growing field with many career opportunities. Here are some points to consider if you are wondering if data science is a good career option:
• High demand: Data science is a highly in-demand field, with many companies seeking qualified professionals to help them make sense of the vast amounts of data they generate. This demand is expected to continue in the coming years, making data science a promising career option.
• Good salaries: Data science professionals are often well-paid, with salaries ranging from around $60,000 to over $150,000 per year, depending on factors such as experience, location, and industry.
• Variety of industries: Data science is a multidisciplinary field that applies to a wide range of industries, including finance, healthcare, retail, and technology. It means that many career opportunities are available in a variety of sectors.
• Opportunity for growth: Data science is a field that is constantly evolving, with new techniques and technologies being developed all the time. It means there is ample opportunity for growth and advancement in the field.
• Versatility: Data science skills are highly transferable and can be applied to many roles, including data analyst, data engineer, and machine learning engineer. This versatility can provide flexibility and opportunities for career advancement.
Overall, data science is a good career option for those interested in using data and analytics to solve complex problems and make informed decisions. With the right combination of skills, knowledge, and experience, data science professionals can enjoy rewarding and lucrative careers in several industries.
Moreover, if you are wondering how long it takes to learn data science, you should try some beginner boot camps, as the duration varies according to the level of expertise you wish to achieve.
Data Science Jobs Roles
Data scientists are the people who design and execute data-driven projects. They use their technical skills to collect, process, analyze and visualize data to find patterns and make predictions. Data scientist is a broad term that can encompass many job roles.
Data scientists use their skills to understand the stories hidden in large datasets (sets of information). They can also help organizations develop new strategies and make more informed decisions by analyzing data from multiple sources. Below are some of the most common ones:
• Machine Learning Engineer
• Data Engineers
• Business Analyst
• Statistician
• Data Architect
• Data admin
• Data Scientist
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. As you can see from the list above, a data scientist requires a few qualifications. With the guidance of dev.to/luxacademy, anyone could become one.
Top comments (1)
Thanks for sharing! I'm thinking about pursuing a career in Data Science and this article helped me!