Data science has become the hottest topic among beginners and professionals who want to develop their careers in the IT industry.
According to the Harvard Business Review, being a data scientist is “The Sexiest Job of the 21st Century” and will grow from 37.9 billion USD in 2019 to 230 billion USD by 2026 as per Linkedin Job Report.
To make a lucrative and rewarding career in data science, first & foremost, you need to understand the data science Roadmap effectively.
If you are a beginner, you can start with the basic Data Science Courses and understand the implementation of data science tools, methods, and techniques.
If you are an IT professional and want to build a long-lasting career in data science, go through applied data science courses to learn about data acquisition, preparation, storage, analytics, and advanced machine learning concepts.
However, building a solid career as a data scientist might be challenging, and acquiring a specific cluster of data science skills is more difficult, so you need a learning programming skills to learn data science.
This article will give you a complete data science roadmap and show you the right path toward developing a successful career in data science. We will explain a step-by-step process of a data science roadmap for achieving a desired objective or goal to become a successful data scientist.
What is Data Science Roadmap? Why do you need it?
Before defining a data science roadmap for 2023, first of all, know about “What is a Roadmap?” and why is it so necessary? A roadmap is a strategic plan or step-by-step approach for achieving your aim and producing desired outcomes.
Even if you are learning something or want to hit your goal, you need a tight schedule or disciplined working environment to win a bright future. Secondly, you talk about data science, it’s a combination of programming, mathematics, statistics, and problem-solving approaches to solve real business problems through data acquiring, cleaning, manipulation, and analysis.
Therefore, the data science roadmap provides the right direction to learn data science and shows you straightway to gain knowledge and skills for building a successful career in the data science field.
Now the question is raised, why do you need a roadmap for data science? So the answer is very clear: before starting any course in data science, you must make a clear strategy for learning data science.
According to the Monster jobs Survey, 96% of IT companies in India are willing to recruit Big Data Analytics professionals by 2023.
Therefore, in the world of data space, the demand for data scientists increases in the market which develops a zeal to know how to start learning data science with perfect planning.
Become a Data Scientist
Everyone knows that becoming a data scientist is not a cup of coffee and needs to be consistent in learning and gaining experience in the practical implementation of analyzing, cleaning, and manipulating real-world data sets to get your dream job.
But why is data science a fruitful field for IT professionals and beginners in the current scenario? Nowadays, the main factor for choosing this field as a profession is the highest pay scale and in-demand jobs across industries.
According to the U.S. News & World Report, the rank of a data scientist is the 3rd best job in technology, 6th best in STEM jobs, and 6th best overall job.
Similarly, Glassdoor ranks it as the 3rd best job in America for 2023. Furthermore, Data from Statista analyzes that the big data market size expects to grow to be worth $103 billion in 2027 compared to $70 billion in 2022.
So, if you have an intellectual mindset to analyze data and love working with real-world data sets, you must go ahead with the latest technological trends in your data science career.
For beginners, it’s become more crucial to learn data science fundamentals and basic concepts to launch a career in this field.
Even if you are a graduate student or IT professional, you need to start with the fundamentals of applied statistics (Sampling techniques, Data Distribution, Central tendency, Dispersion), mathematics (logic and Theories useful for the development of Data Models and Algorithms that are applied to solve the business-related problems), and programming language fundamentals of Python, R and SQL to acquire the skills required to become a data scientist.
The roadmap for data science gives you a structure where you will learn data science step by step and expertise in data science tools and programming languages for a better understanding of data analysis.
With the help of a data science roadmap, you will be more familiar with terminologies such as data formats, schemas, data mining, data exploration, data processing, etc.
Programming Skills to become Data Scientist:
Kickstart your data science career with a solid foundation in this field with core programming. You begin by learning OOPs concepts, Data Structure, Control Structures, concepts, and computing skills required for every activity in data science.
If you are a beginner, you can start learning programming languages like Python, and R to set up your career in data science.
Programming Topics to learn
If you want to build your career in data science, you need to learn programming languages for data science that are used in coding and solving real-world problems. But, before starting to learn any programming language, you need to clear your area of interest and skills required for the job.
There are many languages to learn for data science (such as Python, R, Scala, SQL, JAVA, and Java Script). If you are a beginner and do not have experience in coding, you can start your data science journey with Python programming language.
On the other hand, if you are passionate about data mining in a financial firm, then R is the correct choice.
Nowadays, Python is the most popular programming language among students who are willing to learn data science. On the successful path of the data scientist roadmap, Python is the most required skill set for data science which includes a strong aptitude for quantitative reasoning and experimental analysis.
Although, It is an open-sourced programming language and easy to use with simple syntax to learn for beginners. With the enriched libraries of Python for data science, you can handle data processing and analysis like a cakewalk.
start with basic concepts and functions of python and switch on methods and libraries to get expertise in coding. Moreover, a wide range of libraries of Python for data science (such as NumPy, Pandas, Matplotlib, and Scikit-learn) allows you to deal with Big Data and perform various mathematical, statistical, and logical operations.
In the data science roadmap, R is used for statistical computing and graphics in machine learning. R is an open-source programming language for data science like Python, and it is used for classification, clustering, statistical testing, and linear and nonlinear modeling of datasets.
R programming is known as the most prominent language in the data science world to create applications with the help of libraries (dplyr, tidyr, ggplot2) for data collection, cleaning, wrangling, and analysis.
Apart from this, R programming plays a vital role in advanced research in applied data science. Set up your data science career with Python and R programming languages and go ahead in the data science roadmap.
Data Collection, Cleaning, and Wrangling
The next step of the data science roadmap is data collection, which requires gathering, measuring, and analyzing the relevant information or data from different resources to solve the problem, and questions, evaluate outcomes, and forecast trends and probability.
With the help of Relational Databases, Web Scraping, API, and Python libraries like Pandas, you can collect the data to make it worthwhile. But all the collected data is rarely ready to use.
Messy data is only a source of information, to make it worthwhile, the data cleaning process is necessary to clean duplicate records, and blank fields, and repair structural issues to improve the correctness and consistency of the data by using a multidimensional array, data frame manipulation, sorting, and Python libraries and methods.
After removing erroneous data from your data sets, you need to prepare and transfer the unstructured data into ready-to-use data for further analysis under the Data wrangling process.
On the path of the Data Science Roadmap, Data Engineering is the most in-demand skill to become a data scientist. Data Engineering includes filtering and sorting of collected data to design and build ETL data pipelines that transform the data into a structured format for research.
Data Engineers use different methods such as data collection, data mining, data crunching, data modeling, and data management for modifying the data into a highly usable format.
As a beginner, you can start with C++, Python, Scala, and SQL to build and maintain ETL data pipelines from various resources like My SQL or MongoDB Cassandra.
You can go for further courses in cloud technology to host pipelines on the cloud-based server like Amazon Web Services, Microsoft Azure, Google Cloud Platform, etc.
Exploratory Data Analysis
In the data science roadmap for 2023, the next topic is exploratory data analysis to give a deep understanding of how to analyze and investigate data sets, discover trends, and check assumptions with the help of statistical summaries and graphical representations.
However, EDA is a process to manipulate data sources, spot anomalies, test hypotheses, and make sense of data in hand to make it easier for data engineers.
If you are a beginner or IT professional, you can learn various statistical methods like Mean, Mode, Variance, Standard Deviation, and Correlation to perform univariate and multivariate analyses in a way that is much easier to understand data sets.
Data Visualization is the next step in the data scientist roadmap, where you will learn how to represent data for a better understanding of trends, patterns, and an outlier in data with the help of visual elements like graphs, charts, and maps.
Additionally, Data Visualization includes typical charts, scatter plots, histograms, and pie charts for presenting the data to make top-level decisions.
Furthermore, you will be familiar with the advanced methods and libraries of Python (such as Matplotlib, Seaborn, and Plotly) that provide excellent ways for employees or business owners to present data to make business decisions.
The data can be misinterpreted or misrepresented through the wrong techniques. It is essential to use data visualization tools and tactics to analyze massive amounts of information to make data more understandable.
Statistics and Resources to learn Statistics
Coming up with the strategy for learning data science for beginners is statistical methods. The core foundation of data scientists, having the analytical power that helps in coding to solve real-world problems.
Additionally, statistics is an approach to analyze, visualize, and summarize the data sets for representing and manipulating data in an appropriate format.
you will learn two types of statistics, named descriptive and inferential statistical methodologies. At the initial level, you will know how to analyze quantitative data through graphs, charts, and various statistical fundamentals (like mean, median, mode, standard deviation, and variation).
Further, you will be able to estimate data and test hypotheses with the help of inferential statistical methods.
Mathematics and resources to learn Mathematics
Without mathematics knowledge, you cannot develop the skills required for data scientists. To build a career in data science, you need to have a strong background in mathematics.
Apart from this, machine learning algorithms are an integral part of the data science road that requires math and logical understanding to analyze and discover insights from data.
Whether you are a fresher or interested in learning data science, you need to know the fundamentals of math (like Linear Algebra, Probability, Calculus, Statistics, and so on) to apply data science techniques to solve business problems.
To understand Python and R programming functions, Calculus plays a vital role in understanding and implementing programming methods in coding gracefully.
Moreover, linear algebra for data science is used to solve matrix-vector equations and uses principal component analysis which is essential for learning data science methods.
Is SQL important to Learn for Data Science (SQL Skills for Data Science)
To handle structured data, a data scientist needs SQL skills to write queries and perform various operations on real-world data sets.
If you want to be a data scientist, you must be familiar with SQL and a relational database for collecting and organizing data with the help of data grouping, joining, and other operations.
Without learning SQL, you cannot go straight into a data scientist career because it’s all about data analysis and manipulation. SQL allows you to perform various operations(like SELECT, UPDATE, DELETE, JOIN) on data.
Even, if you want to work on Big Data, SQL provides ultimate techniques such as Hadoop and Spark to organize Big Data effectively.
Although, the combination of SQL and Python in data science is unbeatable for solving real-life problems through tools like SQLite, PostgreSQL, MySQL, and Oracle. Hence, SQL is mandatory to learn data science and to become a data science specialist.
Is it Important to learn Machine Learning and AI as a Data Scientist?
On the path of the data science roadmap, machine learning is the bridge for building a long-lasting career as a data scientist or data analyst.
After gaining a deep understanding of all the concepts mentioned above, you can take the next step with Advanced machine learning courses. By learning Advanced algorithms you will gain more skills that help you to analyze large amounts of data smartly.
However, machine learning can produce accurate results and analysis outcomes with the help of algorithms based on supervised or unsupervised learning.
Although, once you learn core concepts of machine learning and programming languages (like Python, R), then you can explore yourself by learning various algorithms (such as linear and Logistic Regression, Support Vector Machine, Random Forest, kNN, and XGBoost) to develop abilities in machines similar to humans.
Apart from this, Artificial Intelligence is a booster dose for you that accelerates your data scientist career at the top level in the IT industry.
By learning Al algorithms, you can predict patterns and correlations from data that are used in creating predictive models to generate insight from data. Further, Al enables a machine to emulate human behavior to solve complex problems.
The next step in your data science journey is deep learning to give additional value to your portfolio. Deep learning is an essential part of modern artificial intelligence to handle big data through the neural network, which can be impossible in machine learning.
Three types of neural networks (Artificial, Convolutional, and Recurrent Neural Networks) have taken neurons as inputs and passed through the different layers like convolution layers, hidden layers, pooling layers, fully connected layers, etc.
From self-driving cars to advertising, natural language processing, deep learning is used for creating different AI applications.
Neural networks in deep learning are used for image recognition, speech recognition, machine translation, medical diagnosis, object detection, etc.
Furthermore, you can learn how to use various activation functions like Softmax and Relu to get the desired output. Moreover, for big organizations like Google and Amazon that deal with massive data, deep learning algorithms and techniques are used to manage huge data effectively.
Model Building and Deployment
Finally, you have reached the final step of your data scientist roadmap, model building, and deployment. Model building is an important part of data science to develop data sets for training, testing, and production purposes.
You will be familiar with how and when to create supervised and unsupervised models to collect data and examine the factors in answering the questions you asked for solving problems.
Under a supervised model, you will understand how to classify unseen data for forecasting. With an unsupervised model, you will learn about the relationships between different data points within a set for data analysis.
If your data sets pass the testing phases and you are satisfied with them, you can deploy them into the production environment. Additionally, you will learn statistical, mathematical, or simulation models to gain a clear understanding and make predictions.
Overall, Data science is an in-demand field that is used in a robust environment for executing models and workflows (for Example – fast hardware and parallel processing) to make it easier for company owners.
Wrapping up this article with a pleasant smile on your face and hope you enjoyed this beautiful journey by reading the given information towards a complete roadmap for data science. You acquired step-by-step details about how to learn science and how to become a data scientist.
As mentioned above, you can start with fundamentals of data science like mathematics and python language. For further studies, you need to have some required skills to learn advanced data science methodologies and machine learning. These advanced skills can be used to handle huge data and to get higher-paid jobs in organizations.