Best Data Science Training Course in Ahmedabad

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Data Science Training Course Unleashing the
Power of Data
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Introduction of Data Science
Data Science is a multidisciplinary field that
combines scientific methods, algorithms, and
systems to extract insights and knowledge from
structured and unstructured data. It involves
employing techniques from mathematics,
statistics, and computer science to analyze and
interpret data, uncover patterns, make
predictions, and inform decision-making
processes. Data Scientists leverage various tools
and technologies to handle large datasets, apply
machine learning and artificial intelligence
algorithms, and communicate their findings
effectively. By extracting valuable insights from
data, Data Science enables organizations to gain
a competitive edge, optimize processes, and make
data-driven decisions that drive innovation and
improve outcomes in various industries.
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Why Data Science?
Data Science is important because it allows
organizations to unlock the potential of their
data and make informed decisions. By applying
advanced analytics techniques, such as machine
learning and predictive modeling, Data Science
enables businesses to extract valuable insights,
identify patterns, and predict future trends.
This leads to enhanced efficiency, optimized
processes, and improved decision-making across
various industries. Data Science also plays a
vital role in driving innovation, identifying new
business opportunities, and improving customer
experiences. In today's data-driven world,
organizations that harness the power of Data
Science gain a competitive advantage and can
better adapt to the evolving needs of their
customers and markets.
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Course Highlights

• Introduction to data science and data analysis
• Statistics and probability for data analysis
• Data cleaning and preprocessing
• Data visualization and storytelling
• Machine learning algorithms and techniques
• Deep learning and neural networks
• Ethics and privacy in data science
• Advance Excel

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Course Curriculum

• Module 1 - Python Programming
• Introduction to Programming Languages
• Python Real-Time IDEs
• Different modes of Python
• First python Program
• Python File Extensions
• Python Data Types
• Command Line Arguments
• Python Operators
• Control Statements
• Strings
• List Data Structure
• Working with Python Arrays
• Python Tuples

• Set Collection
• Dictionary Collection
• Functions
• Python Modules and Packages
• OOPs Classes Objects
• Exception Handling Types of Errors
• Regular Expressions
• Files in Python
• Date Time Module
• Python Data Base Communications(PDBC)
• Data Analytics Modules
• Python NumPy
• Python Pandas

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Course Curriculum

• Module 2 - Machine Learning
• Introduction to Machine Learning
• Exploratory Data Analysis(EDA)
• Supervised Machine Learning (Regression)
• Logistic Regression
• Ordinal Regression
• Naïve Bayes Classifier Algorithm
• Support Vector Machine
• Decision Tree

• K-Nearest Neighbor
• Random Forest
• Bagging and Boosting
• Dimensionality Reduction
• Time Series Analysis
• ARIMA, SARIMA and ARMA
• Clustering
• Hyper Parameter Optimization

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Course Curriculum

• Module 3 - Deep Learning
• Deep Learning Introduction
• Artificial Neural Network
• Optimization Techniques
• Recurrent Neural Network (RNN)
• Convolution Neural Network (CNN)
• Auto Encoders
• Tensorflow
• OpenCV (Image processing video Processing)

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Course Curriculum

• Module 4 Advance Excel
• Pivot tables and pivot charts
• Conditional formatting
• Remove duplicates
• XLOOKUP
• IFERROR
• MATCH
• COUNTBLANK
• DAYS and NETWORKDAYS
• RANK
• SUMPRODUCT

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Course Curriculum

• Module 5 - Data Visualization
• Seaborn visualization
• Matplotlib visualization
• Pre-processing algorithms
• Regression
• Linear regression
• Logistic regression

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Course Curriculum

• Module 6 - Tableau
• What is Tableau?
• Why Tableau?
• History of Tableau
• Characteristics of Tableau
• Installation Step
• Different versions of Tableau
• What is VizQL
• Use of VizQL in Tableau
• Tableau Architecture and its component

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Course Curriculum

• Module 7 - NLP
• Basics of Natural Language Processing
• Machine Learning Modeling- Navie Bayies
• Word Net and Synsets
• Transformation Models-BERT
• LSTM model-RNN
• Corpus
• Regular expressions for over pattern

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Course Curriculum

• Module 8 - Chatbots
• Chat Bot Architecture
• Under standing Chat Bots Architecture
• Chat Bot Development
• Developing chat Bot Using Python
• Developing chat Bot using Cloud

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Course Curriculum

• Module 9 - Statistics
• Statistics Introduction
• Measure of Center
• Normal Distribution
• Standard Deviation
• Python range()Function Built-in
• Inferential Statistics
• P-value
• ANOVA
• Chi-Square Test
• ARIMA
• Correlation

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Course Curriculum

• Module 9 - Statistics
• Statistics Introduction
• Measure of Center
• Normal Distribution
• Standard Deviation
• Python range()Function Built-in
• Inferential Statistics
• P-value
• ANOVA
• Chi-Square Test
• ARIMA
• Correlation

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304 Aditya Arcade Nr. Choice Restaurant CG Road,
Ahmedabad