A Beginner’s Guide to An Incredible Technology Data Science.pdf

A BEGINNER’S GUIDE TO AN
INCREDIBLE
TECHNOLOGY:
© Copyright 2024. United States Data Science Inﬆitute. All Rights Reserved www.usdsi.org

Data Science refers to the art of drawing insights from Raw Data that assists Business leaders and Decision-makers in
data-driven decision-making.
For all the Students and Young Professionals, curious about what is data science and what’s the career prospects in this
industry, this complete beginner’s guide to data science will answer all your queries.
So, let’s explore the vast world of Data Science.
Data Science is a multidisciplinary field and consists of various fields of expertise including computer science,
mathematics & statistics, and domain knowledge to efficiently extract meaningful insights from data.
According to a report by IBM, 90% of organizations have reported an increase in usage of data science technology for
their business operations in the past year. This increase in the use of data science can be directly attributed to the
increase in the volume of data. The amount of data generated daily is growing at an astounding rate and it is expected
to reach 175 zettabytes by 2025, predicts IDC.
Be it social media interaction, financial transactions, medical records, or scientific research, data holds immense value
for organizations to derive insights that can potentially revolutionize all industries, and transform the way we live,
work, and make decisions.
INTRODUCTION TO DATA SCIENCE
DATA SCIENCE WORKFLOW
Here are the common steps followed in any data science project’s workflow.
PROBLEM STATEMENT AND DATA COLLECTION
The data science journey begins by identifying the particular problem the organizations want to solve with the
help of data and data science. Then data science professionals start their jobs including data engineers and
data scientists finding the relevant source of data. data can be collected through internal databases, external
APIs, web scrapping, physical documents, etc.
STEP
01
WHEN WE HAVE ALL DATA ONLINE IT
WILL BE GREAT FOR HUMANITY. IT IS
A PREREQUISITE TO SOLVING MANY
PROBLEMS THAT HUMANKIND FACES.”
- Robert Cailliau
Informatics Engineer and Computer Scientist
who helped to develop the World Wide Web

EXPLORATORY DATA ANALYSIS (EDA)
EDA is all about knowing the data. in this step, data science professionals use statistical techniques,
visualizations like histograms and scatter plots, and other exploratory techniques to find the patterns, trends,
and relationships in their data.
STEP
02
DATA CLEANING AND PRE-PROCESSING
Often the data collected from real-world situations are messed up. The datasets can have missing values, errors,
or incorrect values. It needs cleaning and preprocessing before it is sent for analysis.
STEP
03
DATA MODELING AND MACHINE LEARNING
Data scientists use machine learning algorithms to learn from data and make predictions. The three main
categories of machine learning are supervised learning, unsupervised learning, and reinforcement learning.
STEP
04
MODEL EVALUATION AND DEPLOYMENT
Once the data science model is ready, they are continuously evaluated and fine-tuned for maximum
performance using metrics like accuracy, precision, recall, etc. This ensures the model is reliable before
deploying for real-world applications.
STEP
05
TOP JOB ROLES IN DATA SCIENCE
Some of the most popular job roles in the data science industry include:
Data Analyst Machine Learning
Engineer
Database
Administrator
Data Engineer Data Scientist/
Senior Data
Scientists
Chief Data
Officer
Machine Learning
Scientist
Business
Intelligence
Analyst
Data Visualization/
Data Storytelling
Specialist
Data and
Analytics
Manager
Data Architect Data Quality
Manager

Data Scientist
Machine Learning Engineer
Machine Learning Scientist
Enterprise Architect
Data Architect
Data Engineer
Business Intelligence Developer
Data Analyst
Statistician
Applications Architect
$155,263
$128,457
$153,065
$156,689
$183,037
$121,919
$136,808
$76,809
$91,361
$145,670
SALARIES OF IN-DEMAND DATA SCIENCE JOBS
POPULAR AND MOST WIDELY USED DATA SCIENCE TOOLS
Data Collection and
Data- Ingestion
Data Cleaning and
Mining
Data Exploration and
Visualization
Data Analysis
Other Tools
Source: Glassdoor
A P A C H E
Job role Annual Average Salary (in U.S.)

CATEGORY TOOLS DESCRIPTION
Programming
Languages
Data Wrangling
and Manipulation
Data Storage
and Management
Machine Learning
Python
R
Pandas
OpenReﬁne
(Google
Reﬁne)
Trifacta
Wrangler
SQL (MySQL,
PostgreSQL)
NoSQL
Databases
(MongoDB,
Cassandra)
Hadoop
Ecosystem
(HDFS, Spark)
Scikit-learn
Dominant language; readable syntax,
extensive libraries (NumPy, Pandas,
Matplotlib)
Popular alternative; strong in statistics
and data visualization
Powerful library for data cleaning,
transformation, and analysis
A user-friendly tool for cleaning and
transforming messy data
Interactive platform for visual
data wrangling
Structured Query Language for
relational databases
Flexible databases for unstructured
or semi-structured data
Scalable framework for storing and
processing large datasets
Comprehensive library for building
and deploying various machine
learning models

CATEGORY TOOLS DESCRIPTION
Machine Learning
Data
Visualization
Cloud
Computing
TensorFlow
PyTorch
Matplotlib
Seaborn
Tableau
Power BI
Amazon
Web Services
(AWS)
Microsoft
Azure
Google Cloud
Platform
(GCP)
Open-source framework for numerical
computation, deep learning, and
large-scale machine learning
Another popular deep-learning framework
with dynamic computational graphs
Versatile library for creating various plots
and charts
Built on top of Matplotlib; a high-level
interface for statistical graphics
Powerful visual analytics platform for
interactive dashboards and data
exploration
Business intelligence tool from Microsoft
for data visualization and reporting
Cloud platform offering various
data science services (SageMaker, Elastic
Compute Cloud)
Cloud platform with data science tools
like Azure Machine Learning and
Azure Databricks
Cloud platform offering data science
services including BigQuery and Vertex AI

BENEFITS OF DATA SCIENCE
Data science can help businesses in numerous ways. Some of the notable benefits of incorporating data science into
business include:
APPLICATIONS OF DATA SCIENCE
Data science isn’t limited to only a few specific sectors. Now organizations from every industry are using it to maximize
their business operations.
Here are the top applications of data science across various industries
Better Data-driven decision-making as it is backed by
Improved efficiency as Data Science helps to Automate tasks, Optimize processes, and
Reduce costs.
Better Customer Experience by personalizing interactions, predicting needs, and
boosting satisfaction
Assist in innovation as Data Science can easily discover hidden patterns. It leads to the
Development of New and Innovative products.
Prevents risk through Predictive Analytics techniques and assists in identifying
potential issues in all industries.
FINANCE HEALTHCARE
RETAIL MANUFACTURING
MARKETING MEDIA & ENTERTAINMENT
Fraud detection, credit risk assessment,
algorithmic trading, personalized financial
products
Personalized medicine, disease prediction,
drug discovery, medical imaging analysis
Inventory management, demand forecasting,
product recommendation, customer
segmentation
Predictive maintenance, quality control, process
optimization, supply chain management
Customer segmentation, targeted advertising,
campaign optimization, social media analytics
Content recommendation, personalized
advertising, audience segmentation, content
creation

GOVERNMENT TRANSPORTATION
SPORTS
Fraudulent tax detection, crime prediction,
resource allocation, public health monitoring
Route optimization, trafﬁc prediction, demand
forecasting, self-driving car development
Player performance analysis, injury prediction,
game strategy creation, optimizing training
regimens
CAREER IN DATA SCIENCE: ROADMAP
EDUCATION REQUIREMENTS OF DATA SCIENCE JOBS
10% 20% 30% 40% 50% 60% 70% 80% 90%
Data Scientist
Associate’s Degree
Machine Learning Engineer
Machine Learning Scientist
Applications Architect
Enterprises Architect
Data Architect
Infrastructure Architect
Data Engineer
Business Intellegence Developer
Statistician
Data Analyst
Bachelor’s Degree
Master’s Degree Ph.D or Professional Degree
Source: Lightcast™ Analyst, 2023

To get started with your data science career, you can follow this simple roadmap:
EDUCATIONAL FOUNDATION
Bachelor's in computer science, information technology,
maths, science, or related field
Master’s in data science, data analytics, statistics, etc.
VALIDATE YOUR EXPERTISE WITH TOP
DATA SCIENCE CERTIFICATIONS
Enroll in data science certification programs
Attend boot camp
Browse free and paid certification courses
START JOB SEARCH
Network with other professionals in this field
Stay active in the data science community and LinkedIn
Reach out to employers
Customize resume specific to job profiles
GAIN RELEVANT DATA SCIENCE
SKILLS AND KNOWLEDGE
BUILD A STRONG PORTFOLIO OF
REAL-WORLD DATA SCIENCE PROJECTS
Get entry-level data science jobs
Join internship
Contribute to open-source projects
Participate in a data science competition
1
2
3
4
5
Following these simple steps can help you get started with your data science career.
CERTIFICATE
Programming language
Data analytics and visualization skills
Soft skills are also important to consider

Data science is an incredible ﬁeld that is growing
rapidly. As more and more organizations seek to
leverage the power of data science, the demand for
data science professionals will soar high in the coming
years. It is therefore recommended that you must enroll
in the best data science certiﬁcation programs, learn
the latest data science skills, empower yourself with
top trends and technologies in the world of data
science, and ace this career path.
CONCLUSION

A Beginner’s Guide to An Incredible Technology Data Science.pdf

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