Data Science Course In Bangalore (1)

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Introduction to Data Science
Dive into the exciting world of data science,
where you'll learn to extract valuable insights
from vast oceans of information. This
comprehensive course will equip you with the
skills to become a versatile data professional,
poised to tackle complex challenges and unlock
the power of data.
by Ansar Alam
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Overview of the Course
Fundamentals of Python
Data Acquisition and Preprocessing
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Master the essential programming language for
data science, Python, and learn to write clean,
efficient code.
Acquire data from various sources, clean, and
transform it to prepare for analysis.
Exploratory Data Analysis
Machine Learning Algorithms
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Uncover hidden patterns, trends, and insights
through in-depth data exploration.
Dive into the world of predictive modeling and
learn to apply cutting-edge machine learning
techniques.
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Fundamentals of Python Programming
Syntax and Data Types
Control Structures
Functions and Modules
Familiarize yourself with the fundamentals of
Python's syntax and the various data types you'll
encounter.
Master conditional statements, loops, and other
control structures to write robust and flexible
code.
Learn to create and utilize reusable functions,
as well as leverage the extensive Python module
ecosystem.
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Data Acquisition and Preprocessing
Web Scraping
API Integration
Develop the ability to extract data from websites
using Python libraries like BeautifulSoup and
Scrapy.
Learn to interact with various APIs to fetch data
programmatically and enhance your data sources.
Data Cleaning
Feature Engineering
Develop strategies to handle missing values,
remove outliers, and ensure data integrity for
reliable analysis.
Transform raw data into meaningful features that
can improve the performance of your machine
learning models.
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Exploratory Data Analysis
Univariate Analysis
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Examine individual variables to understand their
distribution, central tendency, and dispersion.
Bivariate Analysis
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Explore relationships between two variables,
identifying patterns and potential correlations.
Multivariate Analysis
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Uncover complex relationships and interactions
among multiple variables for deeper insights.
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Machine Learning Algorithms
Regression
Classification
Predict continuous target variables using linear,
polynomial, or other regression techniques.
Categorize data into distinct classes using
algorithms like Logistic Regression, SVM, and
Decision Trees.
Clustering
Recommendation
Group similar data points together without prior
knowledge of the classes, using K-Means or DBSCAN.
Build personalized recommendation systems using
techniques like Collaborative Filtering and
Content-Based Filtering.
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Model Evaluation and Deployment
Model Validation
Hyperparameter Tuning
Model Deployment
Assess the performance of your models using
techniques like cross-validation, confusion
matrices, and ROC curves.
Optimize model parameters to improve accuracy and
generalization through methods like grid search
and random search.
Learn to package and deploy your machine learning
models as scalable, production-ready applications.
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Career Opportunities in Data Science
Data Analyst
Uncover insights and drive decision-making
through data-driven analysis.
Data Engineer
Design and build data pipelines, infrastructure,
and systems to support data-centric solutions.
Machine Learning Engineer
Develop and deploy advanced machine learning
models to solve complex problems.
Business Intelligence Specialist
Leverage data to provide strategic
recommendations and support business objectives.