Data Science Roadmap 2023: The Ultimate Guide to Master

Data Science Roadmap 2023

After knowing the importance of Data Science on the internet i.e. Blogs or YouTube Channels, one may show interest to learn Data Science. The geek may face the problem that what one (he/she) should learn. The geek may show the desire to have a complete roadmap of Data Science in 2023.

 

After reading this article, I can assure you that the geek (student) would be well known about the prerequisites i.e. tools, techniques and technologies, and steps to pursue tutorials and certifications related to data mining, munging, modeling, and machine learning.

 

Here comes the importance of this article. This roadmap of data science would first introduce you to the tools used in the field of data science. Further, it will introduce you to the basic knowledge of data science including key terms, techniques, and technologies. Moreover, this will put forward a complete step-by-step roadmap for data science.

 

I am a professional Software Developer and Blogger. I have 7 years of experience in blogging. I have collected all the stats and data used in this article from the "Python Data Science Handbook by Jake VanderPlas" and IBM Data Science. Hence, the conformity of data is valid.

 

Unfolding the Topic:

Data Science is a booming computer field. Its importance is increasing day by day as tons of data is created on the internet every day. Such an expanding pace of the field has created a desire in youth about mastering it. It is almost impossible to master the techniques and tools of data science without a proper roadmap.

This piece of writing would provide ease in the process of mastering data science in the form of providing basic knowledge and a complete roadmap.

Pre-Requisites:

Required Tools:

1). Download Anaconda:

Anaconda Distribution is a stack of fundamental Software, and Packages of Data Science, Artificial Intelligence, and Machine Learning. Conda is Package and environment management system which installs and manages all anacondas’ packages.

The main Data Science related Packages of Anaconda include:

* Jupyter Notebook:

The Jupyter Notebook is an interactive web page that runs on the server. It combines the Live Code, Equations, Text, Video Links, and Pictorial Representations i.e. Graphs, Histograms, and Line Plots in One UI Page.

* NumPy:

NumPy is a Python Library for performing operations on arrays. Array indexing, Slicing, concatenation, etc can be achieved by Pandas Data Frames by NumPy.

* Pandas:

Pandas is Data Science Library used for Data Analysis and Manipulation. It is built on top of Python Programming Language.

* Matplotlib:

It is a very useful Python Library for visualizing stats in pictorial form. It has a number of different graphs and plots to present data in pictorial form.

 

2). Python-enabled Machine:

As in this article, we are planning to master Python Data Science. A Python-installed machine preferably Python-3 is a pre-requisite. By the way, anaconda distribution has Python in its stack and one can download Python from Anaconda. It can also be downloaded from the Python Website also.

 

Basic pre-requisite Knowledge:

1). Statistics:

To become a successful data scientist, sufficient knowledge of Statistics is mandatory. As Data Science is the field of numbers and computations. The basic concepts of Mathematics and Statistics are necessarily required for interacting with datasets and filtering stats from rough and noisy data.

 

2). Python:

Python is a high-level, general-purpose, interpreted, and structured programming language. It is very famous for its easier syntax. Python is a modern programming language with the best architectural features i.e., garbage-collection, dynamically typed, object-oriented, platform-independent, and easier debugging. Its Data Structures (i.e. Data Types, Dictionaries, Tuples, and Array Functions) and wide range of Scientific Computational libraries made it the best solution for Data Science.

 Also Read: MERN Stack Development

 

Step-by-Step Complete Roadmap of Data Science in 2023:

1). Python Core Programming:

(15-20 Days)

Learning Python is a very important Layer of the Data Science Roadmap. Basic Core Python is mandatory for performing tasks related to data munging and data validation.

Following Python Topics Must be Covered as part of the roadmap:

• Data Types
• Loop
• Conditionals
• String Functions
• Lists
• Dictionaries
• Tuples

 

2). Get Familiar with NumPy:

(10 Days)

Arrays of NumPy are necessarily important for understanding the Panda's Operations of big data. NumPy is a prerequisite for Pandas to work. Hence, Learning NumPy is a mandatory Layer of the roadmap to becoming a Data Scientist.

Below NumPy Topics are necessary for understanding Pandas Calculations:

• Basic NumPy Arrays (Array Indexing, Slicing, and Concatenation)
• Sorting Arrays

 

3). Pandas Data Manipulation:

(30-40 Days)

Pandas is Python Library used for Data Analysis and Data Manipulation. It is used to import and export data. Data Frames are the Panda's Data Objects. All operations are applied to the Data Frames of Pandas.

The following are the must-know features of Pandas:

• Import and Export Data from CSV or Excel to Data Frame and Vice-versa.
• Data Frames
• Data Indexing and Selection
• Handling Missing Data
• Concat, Append, Merge, and Join Datasets
• String Operations
• Time Series

 

4). Matplotlib:

(5-10 Days)

Matplotlib is a Python Tool / Library used for visualizing the stats and data in the form of charts, plots, lines, and other forms of data visualization.

Below are the necessary pictorial tools of Matplotlib:

• Simple Plots
• Visualizing Errors
• Histograms, Binning, and Density
• Color Bars and Subplots
• Three-Dimensional Plotting

 

5). Machine Learning:

(50-60 Days)

Machine Learning is in itself a field of Computer Science. It includes different Python libraries to train a Machine Learning Model. Scikit-Learn and TensorFlow are used to train machine learning models and export the ML Models.

The following are the necessary concepts for mastering Machine Learning:

• Scikit-Learn
• Model Validation
• Feature Engineering
• Linear Regression
• Decision Tree and Random Forest
• K-Means Clustering
• ML Model Training
• Exporting ML Model

 Also Read: Django Framework 

 

Final Words:

To windup, it can be said that all the necessary layers or steps of the data science roadmap have been completely discussed. From now one should refer to the book "Python Data Science Handbook by Jake VanderPlas" or any other tutorial.

Data Science is a growing field in the Digital World. Hence one should pursue these steps of the Data Science Roadmap to become a data Scientist.