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.