Data Science for Beginners: 2023 - 2024 Complete Roadmap
Published: 2023-10-01 08:18:45

As organizations are generating and storing more and more data, they are looking to hire professionals who can dig into this overwhelming amount of Data to derive valuable insights that can help drive business decisions. This has led the demand for Data Scientists to surge in the past few years. Data Scientist are one of the highest-paid professionals across the industries, and Data Science offers a promising and lucrative career path. As per LinkedIn job reports, the Data Science industry is expected to grow from 37.9 billion USD in 2019 to 230 billion USD by 2026. In fact, Data Scientist has already been regarded as the sexiest job of the 21st century by Harvard Business Review. Due to this, Data Science has become one of the hottest and trending topics among students and professionals who want to build a career in this field. However, learning a new discipline can be challenging and overwhelming sometimes, so to mitigate this, there is a need for a solid educational plan or learning roadmap. A learning roadmap can be defined as a strategic plan with various steps to achieve a desired objective or goal.


Why Become a Data Scientist?
Data Scientists are in demand worldwide and in industries. Based on a survey by Monster jobs, 96% of the companies in Kenya are looking to hire professionals to fill Big Data Analytics roles by 2023. This demand is expected to grow as we are set to generate more and more data with the arrival of the Internet of Things (IoT), and businesses become more reliant on valuable insights derived from this data for their success and growth.

Also, Data Scientists are one of the highest paid professionals across the industries. Though, the salary of a Data Scientist depends on multiple factors such as years of experience, education, skillset, company, and location. Some companies pay higher to Data Scientists having specialized skills such as Computer Vision, Natural Language Processing, etc.

**The Data Scientist Roadmap**
If you have decided to build a career in Data Science, let’s get into the learning roadmap to become a Data Scientist. A Data Scientist brings together concepts of Software Engineering, Statistics, and the business world to dig into the data to identify valuable insights. We have listed a few steps to help you learn and master the skills required to become a Data Scientist. These steps have their own learning curve based on the complexities involved. So, it will take different times to learn and master each step. The pyramid in the below figure depicts high-level skills required for a Data Scientist’s job in order of the complexity involved and common usage across industries. The Data Scientist Roadmap


                     

**Learn Python**

Every Data Scientist's job requires expertise in one of the programming languages to perform various Data Science tasks. The most common languages Data Scientists use are Python and R. If you are a beginner, learning Python is strongly recommended for Data Science over any other programming language. One of the main reasons Python is widely used and most popular in the Data Science community is its ease of use and simplified syntax, making it easy to learn and adapt for people with no engineering background. Also, you can find a lot of open-source libraries along with online documentation for the implementation of various Data Science tasks such as Machine Learning, Deep Learning, Data Visualization, etc.
Now you know why you should learn Python as a first step to becoming a Data Scientist, let’s get into specific programming topics which you must include in your learning roadmap.
Data Structures (Various Data Types,Lists, Tuples, Dictionary, Array, Sets, Matrices, Vectors,, etc.)
Define and Writing User Defined Functions
Different kinds of Loops and conditional statements such as If, else,, etc.
Searching and Sorting algorithms
SQL concepts - Join, Aggregations, Merge, etc.
**Learn Python Libraries for Data Science**

One of the reasons for the popularity of Python in the Data Science community is that it provides numerous libraries to implement any kind of Data Science related tasks. A few of the most common libraries used by Data Scientists are -
NumPy
NumPy is a library that provides various methods and functions to handle and process large Arrays, Matrices, and Linear Algebra.
It stands for Numerical Python, and this library provides vectorization of various linear algebra and mathematical functions required to work on large matrices and arrays. Vectorization enables functions to apply operations on all elements of a vector without needing to loop through and act on each item, one at a time, resulting in enhanced execution speed and performance.
Pandas
Pandas is the most popular Python library among Data Scientists. This library provides many useful in-built functions to perform data manipulation and analysis on large amounts of structured data. Pandas are a perfect tool when it comes to Data Wrangling.
It supports two data structures - Series and Dataframe.
Series is a one-dimensional array and capable of holding data of any type (integer, string, float, python objects, etc.). A Data frame in Pandas is a heterogeneous two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns like an excel spreadsheet or SQL table. Pandas DataFrame is capable of having columns with multiple data types.
Matplotlib
Data Visualization is one of the key steps in implementing any Data Science solution. Matplotlib is a handy library that provides methods and functions to visualize data such as graphs, pie charts, plots, etc. You can even use the matplotlib library to customize every aspect of your figures and make them interactive.
Seaborn
It is another Python visualization library that provides many in-built functions for different visualization methods such as histograms, bar charts, heatmaps, density plots, etc. Its syntax is much easier to use compared with matplotlib and provides aesthetically appealing figures.
SciPy
You would be required to perform a lot of statistical analysis as a Data Scientist, such as performing EDA on the data using statistical methods such as mean, standard deviation, z-score, p-value test, etc. SciPy will provide you with various methods and functions for the implementation of statistical and mathematical concepts required in Data Science.
Scikit-Learn
It is a Machine Learning Python library that provides a simple, optimized, and consistent implementation for a wide array of Machine Learning techniques.
**Learn About Data Collection and Wrangling**

Once you have grasped the fundamentals of Python programming language, you can move on to the next step, learning about Data Collection and Wrangling.
Data Collection is the process to gather relevant data for further analysis from a variety of sources such as Relational Databases, Web Scraping, APIs, etc. Pandas library in Python provides various methods to collect data from different sources.
Once data is collected, the next step is Data Wrangling, which is preparing and transforming data in an easier way to further analyze. It requires cleaning the data, preparing the data, feature engineering, etc. Pandas and NumPy libraries can help you with methods and functions needed for Data Wrangling and manipulation.
**Learn About Exploratory Data Analysis, Business Acumen, and Storytelling**

The next step is to learn and master Data Exploration and Storytelling skills that will enable you to identify trends, insights, etc., and communicate them to senior management in a way that is much easier to understand.
Few of the topics you should have in your learning roadmap include -
Exploratory Data Analysis (EDA) - It includes exploring the data using various statistical methods such as Mean, Mode, Variance, Standard Deviation, Correlation,, etc. In this step, you will learn to build hypotheses, perform univariate and multivariate analyses,, etc.
Data Visualization - It includes data exploration using visual methods such as plotting histograms, bar charts, box plots, and density plots to identify trends and patterns within the data. Matplotlib, Seaborn, Plotly, etc. are a few of the Python libraries that can help you implement these methods.
Dashboards - Creating dashboards using tools such as PowerBI, Tableau, etc. is the most efficient way to communicate your findings and recommendations to senior management. It will make your presentation more visually appealing and easier to understand.
Business Acumen - While you work on performing exploratory data analysis on the data, you should keep working on asking the right set of questions that can help businesses achieve the target.
**Learn About Data Engineering**

Data Engineering is the field of building data infrastructure that will provide Data Scientists formatted data that is further easy to analyze by designing, building, and maintaining ETL data pipelines. Though it is not a mandatory requirement to learn for a Data Scientist, having a good understanding of Data Engineering is a big plus when being considered for the Data Scientist job.
Data Engineers use advanced programming languages such as C++, Python, Scala, SQL, etc. to build ETL pipelines on raw data collected from different kinds of databases such as MySQL, MongoDB, etc. These pipelines can be hosted on a cloud-based platform such as AWS, Microsoft Azure, Google Cloud Platform (GCP), etc.
Learn About Applied Statistics and Mathematics

Statistics and Mathematics are integral to Data Science and any Machine Learning algorithm. For a Data Scientist, it is a must to have a sound understanding of various statistical and mathematical concepts involved in Data Science.
Few of the topics you should include in your Data Scientist learning roadmap -
Descriptive Statistics - It is a powerful method to summarize the data by using statistical methods such as Mean, Mode, Variance, Standard Deviation, etc.
Inferential Statistics - This field includes hypothesis testing by performing inferential tests such as A/B testing, p-value statistics, etc.
Linear Algebra and Calculus - This field will help you understand various mathematical concepts in Machine Learning algorithms such as Gradient Descent, Loss Function, Optimization, etc.
**Learn About Machine Learning and AI**

Once you have gained a deeper understanding of all the concepts mentioned above, you can move on to learn and understand Machine Learning algorithms.
Below are categories of Machine Learning algorithms used in a Data Scientist’s job -
Supervised Learning - These algorithms learn the pattern in the data when a target variable is present. It includes Regression and Classification techniques. You should have popular ML algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, KNNs,, etc. in your learning roadmap.
Unsupervised Learning - These algorithms are used when no target variable is available. You should study K-Means Clustering, PCA, Association Mining,, etc. under this category.
Deep Learning - It is a subfield within Machine Learning research that models data using Neural Networks. Neural Networks are nothing but mathematical models mimicking the human brain. Deep Learning has enabled Data Scientists to process and model complex data such as Images, texts, etc. You should have good knowledge of Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM), Autoencoders,, etc. for a Data Scientist job.
Do any of these skills seem like alien territory? If you'd like to learn them, check out Scaler’s Data Science program.

**Points to Remember**
Though having a degree in Computer Science discipline is considered an added advantage but it is not a mandatory requirement as long as you have learned and mastered the right set of skills.
Having domain expertise or knowledge is always considered plus as it helps you leverage the data in the best way.
Good verbal and written communication skills help you collaborate with multiple stakeholders and communicate your findings and recommendations to them.
It can be intimidating to learn Data Science as it is a vast area. So focus on understanding the basic fundamentals and gradually improve your skills to learn advanced concepts.
Sharpen your theoretical skills by working on projects with real-world data. Remember that organizations always prefer practical applications over theoretical knowledge.
You should always track your learning process. For example, taking assignments post learning a new concept will help you understand whether you are on the right path or not.
Staying updated with the ongoing research will help you stand out from the crowd.

**Conclusion**
Data Scientists are in high demand and are one of the highest-paid professionals in the Data Science field. With the ever-growing data, business organizations have increased investments in improving their data infrastructure and implementation of data science solutions. Due to this, this demand is expected to grow in the next decade as well. The U.S. Bureau of Labor Statistics has estimated a 22 percent growth in data science jobs during 2020-2030. If you wish to build a career as a Data Scientist, you can create a strong learning plan using this guide that can help you get your first Data Scientist job. Post learning the skills, make sure to work on diverse sets of Data Science projects to apply your skills as practical applications are always preferred over theoretical knowledge for a Data Scientist job.