Start your Data Science career journey with an extensive & practical Data Science course designed for young professionals and recent college graduates. We provide in-depth knowledge of Python’s data analytics tools and techniques in this Data Science certification program.

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Career in
Python & Data Science
Profiles | Opportunities | Required Skills

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What is Data
Science?
Data science encompasses preparing data for analysis, including
cleansing, aggregating, and manipulating the data to perform
advanced data analysis.
Data science combines multiple fields, including:
● Statistics & scientific methods.
● Artificial intelligence (AI) & Data analysis.
to extract value from data.

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Data Science
A way towards Machine Learning
● Companies are sitting on a treasure trove of data.
● As modern technology has enabled the creation and storage of
increasing amounts of information, data volumes have exploded.
● It’s estimated that 90% of the data in the world was created in the last
two years.
● For example, Facebook users upload 10 million photos every hour.
The wealth of data being collected and stored by these technologies can
bring transformative benefits to organizations and societies around the
world—but only if we can interpret it. That’s where data science comes in.

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Purpose of Data Science
01 Data science reveals trends and produces insights that
businesses can use to make better decisions and create more
innovative products and services.
02
It enables machine learning (ML) models to learn from the
vast amounts of data being fed to them, rather than mainly
relying upon business analysts to see what they can discover
from the data.
03 Data is the bedrock of innovation, but its value comes from
the information data scientists can glean from it, and then act
upon.

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Difference between
Data Science, A.I, M.L & Deep Learning
● AI means getting a computer to mimic human behavior in some
way.
● Data science is a subset of AI, and it refers more to the overlapping
areas of statistics, scientific methods, and data analysis—all of which
are used to extract meaning and insights from data.
● Machine learning is another subset of AI, and it consists of the
techniques that enable computers to figure things out from the
data and deliver AI applications.
● Deep learning which is a subset of machine learning that enables
computers to solve more complex problems.

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Data science transforms business
● Analyze call centre data to identify churning
customers so marketing can retain them.
● Analyze traffic patterns, weather, and other factors to
improve delivery times and lower costs.
● Analyze medical test results and patient symptoms to
help doctors diagnose and treat diseases faster.
● Predictive maintenance improves supply chain
efficiency.
● Recognize suspicious behaviours and actions to detect
financial fraud.
● Improve sales by making customer recommendations
based on past purchases.

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Data Science Process
● Planning.
● Building a data model.
● Evaluating a model.
● Explaining the models.
● Deploying it.
● Monitoring and testing.
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Tools used in Data Science
Apache Spark
An open source data
processing and analytics
engine that can handle large
amounts of data
Pandas
Another popular open source
Python library, pandas typically is
used for data analysis and
manipulation.
NumPy
NumPy is an open source Python
library that's used widely in scientific
computing, engineering, and data
science and machine learning
applications.
Python
Python is the most widely used
programming language for
data science and machine
learning
01
02
03
04
Complete List Here

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Data Science Life Cycle
● A data science life cycle is an iterative
set of data science steps you take to
deliver a project or analysis.
● Because every data science project
and team are different, every specific
data science life cycle is different.
● However, most data science projects
tend to flow through the same
general life cycle of data science
steps.

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Data Scientist: Expected Skills
Technical Skills
● Statistical analysis and
computing
● Machine Learning
● Deep Learning
● Processing large data sets
● Data Visualization
● Mathematics
● Programming
● Statistics
● Big Data
Non Technical Skills
● Strong Business Acumen.
● Strong Communication Skills
● Great Data Intuition.
● Computing knowledge and
smartness.
● Storytelling Skills.
● Structured Thinking.
● Curiosity.

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Data Science Course Content
Module 3
● Introduction of NumPy and
Pandas.
● Library installation.
● Data visualization libraries like
(matplotlib and seaborn).
● Introduction of Power BI.
● Introduction to SQL.
Module 4
● Introduction to Statistics
● Statistical Function like (Mean ,
Median , Mode).
● EDA parts (Data Wrangling).
● Correlation Matrix.
● Normal Distribution , Skewness ,
Bell curve.

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Data Science Course Content
Module 1
● Introduction of data science
● Data Science life cycle
● Tools of data science
● Introduction of python
● Introduction of Machine Learning
Module 2
● Introduction to Python.
● Basic operations.
● Variable assignment.
● Functions in Python.
● Condition and loops.
● Data structure.
● Array , Object , Exception handling.
● File handling.

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Data Science Course Content
Module 5
● Introduction of Machine Learning and
categories
● Data Preprocessing
● Data Preparation
● Machine Learning types and algorithms
● Introduction Parameter in Machine Learning
and PCA
● Introduction on Github and Kaggle
● Implementation of case study

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Career in Data Science in India

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Job Profiles in Data Science & Analysis
● Data Scientist.
● Data Analyst.
● Data Engineer.
● Data Storyteller.
● Data Architect.
● Machine Learning Scientist
● Machine Learning Engineer
● Business Intelligence Developer
● Database Administrator
● AI Specialist.
● Deep Learning professionals.
● NLP specialists.

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Data Scientist Salary in India
Freshers & Experienced

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Data Scientist Salary in India
Freshers & Experienced

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Top Rated &
Expected Skills

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Salaries By Companies

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Data Science Industry Stats

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Data Science Course Overview
● Data Science life cycle.
● Data Science tools.
● Data visualization libraries like Matplotlib and Seaborn.
● Introduction to Statistics.
● Introduction to SQL.
● Data processing and preparation.
● Introduction to Github and Kaggle.
https://softspacesolutions.com/data-science-course/
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