Data Science with Python Training in San Antonio

Name: Data Science with Python Certification Course
Item: Data Science with Python Certification Course
Rating: 5
Author: Sujit Samal

Module 1: Introduction to Python for Data Science

Topics:

Set operations
List comprehensions
Generator functions
Using modules
Regex patterns
Special collections
Map & filter
Custom exceptions

Hands-On/Demo:

Writing a “Hello World” script
Manipulating lists and dictionaries
Reading a CSV file

Skills:

Python programming
Data science environment setup

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Module 2: Working with Python Programming

Topics:

OOP concepts
Class inheritance
Context managers
Unit testing
API requests
Code profiling
Logging basics
JSON handling
Project packaging
Type hints

Hands-On/Demo:

Using regex for data cleaning
Creating a generator
Building a custom module

Skills:

Intermediate Python techniques
Efficient code structures

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Module 3: Advanced Python Programming for Data Science

Topics:

NumPy arrays
Vector operations
Math functions
Series handling
DataFrames
Dataset merging
Missing values
Pivot tables
Data summaries
Memory tuning

Hands-On/Demo:

Building a preprocessing class
Fetching API data
Writing a unit test

Skills:

Advanced Python programming
Modular code developmen

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Module 4: Data Analysis with NumPy and Pandas

Topics:

NumPy arrays
Vector operations
Math functions
Series handling
DataFrames
Dataset merging
Missing values
Pivot tables
Data summaries
Memory tuning

Hands-On/Demo:

NumPy array calculations
Cleaning data with Pandas
Creating a pivot table

Skills:

Data manipulation
Basic statistical analysis

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Module 5: Data Visualization and Preprocessing Techniques

Topics:

Matplotlib Plotting
Seaborn Visualization Styles
Line and Bar Charts
Histogram Analysis
Web Scraping Basics
Missing Data Treatment
Feature Scaling Techniques
Encoding Categorical Data
Data Storytelling Approaches

Hands-On/Demo:

Creating Seaborn plots
Scraping website data
Normalizing a dataset

Skills:

Data visualization
Data preprocessing

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Module 6: Statistical Methods for Data Science

Topics:

Descriptive stats
Variance and standard deviation
Probability
Normal distribution
Hypothesis testing: t-tests
Correlation: Pearson coefficient
Outlier detection: z-score
Sampling: random sampling
Statistical visualization
P-values: significance

Hands-On/Demo:

Conducting a t-test
Visualizing correlations
Detecting outliers

Skills:

Statistical analysis
Result interpretation

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Module 7: Fundamentals of Machine Learning

Topics:

CRISP-DM process
ML categories
Python for ML
ML tools
Data lifecycle
Evaluation
Feature basics
AI ethics
Industry insights

Hands-On/Demo:

Setting up an ML project
Exploring a dataset

Skills:

ML workflows
Industry trends

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Module 8: Supervised Learning – Regression Analysis

Topics:

Linear regression
Gradient descent
Polynomial regression
Ridge regression
Error metrics
R-squared
Cross-validation
Residual analysis
Feature selection
Overfitting mitigation

Hands-On/Demo:

Building a linear regression model
Evaluating with RMSE

Skills:

Regression modeling
Model evaluation

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Module 9: Supervised Learning – Classification Fundamentals

Topics:

Logistic regression
Binary labels
Decision trees
Confusion matrix
Precision & recall
ROC curve
Overfitting
Feature ranking
Model validation
Class imbalance

Hands-On/Demo:

Logistic regression model
Decision tree visualization

Skills:

Binary classification
Evaluation metrics

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Module 10: Supervised Learning – Advanced Classification

Topics:

Random forests
SVM
XGBoost
Grid search
Random search
SHAP values
SMOTE
Model stacking
Association rules
Recommendation engines
Model evaluation

Hands-On/Demo:

Random Forest model
Using SHAP for insights
Building Apriori rules

Skills:

Advanced classification
Interpretability, recommendations

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Module 11: Unsupervised Learning and Clustering Techniques

Topics:

K-Means clusters
Elbow method
Hierarchical clustering
DBSCAN logic
PCA reduction
Anomaly detection
Silhouette score
Segmentation use
Cluster visuals

Hands-On/Demo:

K-Means clustering
Applying PCA
Detecting anomalies

Skills:

Unsupervised learning
Dimensionality reduction

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Module 12: AutoML and No-Code Data Science Solutions

Topics:

AutoML tools
DataRobot
KNIME workflows
H2O.ai models
Synthetic data
Rapid prototyping
AI fairness

Hands-On/Demo:

DataRobot model building
KNIME workflow creation
Generating synthetic data

Skills:

AutoML prototyping
No Code workflows

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Module 13: Reinforcement Learning Essentials (Self-paced)

Topics:

Agent-Environment Interaction
OpenAI Gym Setup
Markov Decision Process
Q-Learning Fundamentals
Exploration-Exploitation Tradeoff
Epsilon-Greedy Strategy
Reward Shaping Concepts
Reinforcement Learning Applications
Q-Table Implementation
Reinforcement Learning Limitations

Hands-On/Demo:

Q-Learning in a game
OpenAI Gym experiment

Skills:

RL algorithms
Practical applications

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Module 14: Time Series Analysis and Forecasting Methods (Self-paced)

Topics:

Time Series Components
Stationarity Testing (ADF)
ARIMA Model Parameters
Forecasting with Prophet
Forecast Error Metrics
Backtesting Techniques
Trend Visualization Methods
Confidence Interval Analysis
External Variable Integration
Model Selection Strategies

Hands-On/Demo:

ARIMA model
Prophet forecasting
Visualizing trends

Skills:

Time series analysis
Forecasting

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Module 15: Machine Learning on Cloud Platforms (Self-paced)

Topics:

Cloud ML Introduction
AWS SageMaker
Google Cloud AI
Azure ML
Cloud storage
Serverless ML
Model deployment
Scalability

Hands-On/Demo:

Training a model in SageMaker
Deploying with Google Cloud AI
Using S3 for data storage

Skills:

Cloud-based ML
Scalable model deployment

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Module 16: MLOps Fundamentals (Self-paced)

Topics:

MLOps Introduction
CI/CD for ML
Flask API Deployment
MLflow Model Tracking
Docker Containerization
Model Drift Monitoring
Model Lifecycle Management

Hands-On/Demo:

Deploying with Flask
MLflow pipeline setup

Skills:

MLOps practices

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Why Learn Python for Data Science in San Antonio?

Python has been one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger. It has evolved as the most preferred Language for Data Analytics and the increasing search trends also indicate that it is the Next Big Thing and a must for Professionals in the Data Analytics domain.

What are the objectives of our Data Science with Python Training in San Antonio?

After completing this Data Science using Python Certification course in San Antonio, you will be able to:

Programmatically download and analyze data
Learn techniques to deal with different types of data – ordinal, categorical, encoding
Learn data visualization
Using I python notebooks, master the art of presenting step by step data analysis
Gain insight into the 'Roles' played by a Machine Learning Engineer
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Perform Text Mining and Sentimental analysis
Explain Time Series and its related concepts
Gain expertise to handle business in future, living the present

Edureka offers the best online course for Python Data Science. Enroll now with our data Science with Python training and get a chance to learn from industrial giants.

Who should go for this Python Data Science online course in San Antonio?

Edureka’s course is a good fit for the below professionals:

Programmers, Developers, Technical Leads, Architects
Developers aspiring to be a ‘Machine Learning Engineer'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine
Learning (ML) Techniques
Information Architects who want to gain expertise in
Predictive Analytics
Professionals who want to design automatic predictive models

What are the prerequisites for this Data Science with Python Training in San Antonio?

The pre-requisites for Edureka's Python Data Science course training includes the fundamental understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the Data Science with Python certification course.

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What is the main purpose of using Python?

Python is designed to be a high-level, versatile programming language that is suitable for a diverse array of applications, such as web development, software development, data analysis, and automation. It is powerful for complex tasks and accessible to novices due to its extensive libraries and simple syntax.

How much Python is required for data science?

You don't need to be a Python specialist to work in data science, but you do need to know about the basics and essential libraries.

Is Python enough to become a data scientist?

Python is an important and commonly used language in data science, but it's not enough on its own to make you a data scientist. Python is excellent because it has a lot of libraries, like as Pandas, NumPy, and Scikit-learn, which are essential to interacting with data, analyzing it, and learning how to use machines. However, data science needs a wider range of abilities, including statistical concepts, data visualization, communication, and domain expertise.

What is the use of data science?

Data science is the process that extracts useful information and insights from data so that clients may make better decisions and solve problems in numerous domains.

Does data science have scope in the future?

Yes, the future of data science is extremely bright. Data is becoming more and more common, and businesses need people who can make decisions based on data. This is causing the area to grow rapidly and to need skilled employees.

What is the role of a data scientist?

A data scientist's job is to look at and understand complicated data in order to find useful information that can help businesses make decisions. They use a mix of computer science, machine learning, and statistical analysis abilities to connect raw data with strategies that can be put into action.

What is the cycle of data science?

The data science cycle, or data science lifecycle, is a way to solve problems with data that is organized. There are several processes that are repeated, such as describing the problem, gathering and preparing data, exploring and modeling the data, evaluating the model, and sharing the results. The cycle isn't a straight line; it's an iterative process where new information learned at one level may cause you to go back to prior phases.

What are the five steps of the data science process?

Defining the problem, obtaining the data, exploring the data, modeling the data, and communicating the results are the five stages of the data science process.

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Testimonial Reviews

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Edureka Data science course provided me a very good mixture of theoretical and practical training. T ... Read More

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Very good courses covering lots of topics. Excellent class delivery and lecture notes. I really lear ... Read More

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Data Science with Python Course in San Antonio

Why should you take Data Science with Python Training Course in San Antonio??

Instructor-led Data Science with Python Certification Course live online classes

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Data Science with Python Course in San Antonio Curriculum

Module 1: Introduction to Python for Data Science

Module 2: Working with Python Programming

Module 3: Advanced Python Programming for Data Science

Module 4: Data Analysis with NumPy and Pandas

Module 5: Data Visualization and Preprocessing Techniques

Module 6: Statistical Methods for Data Science

Module 7: Fundamentals of Machine Learning

Module 8: Supervised Learning – Regression Analysis

Module 9: Supervised Learning – Classification Fundamentals

Module 10: Supervised Learning – Advanced Classification

Module 11: Unsupervised Learning and Clustering Techniques

Module 12: AutoML and No-Code Data Science Solutions

Module 13: Reinforcement Learning Essentials (Self-paced)

Module 14: Time Series Analysis and Forecasting Methods (Self-paced)

Module 15: Machine Learning on Cloud Platforms (Self-paced)

Module 16: MLOps Fundamentals (Self-paced)

Data Science Python Training in San Antonio Description

Why Learn Python for Data Science in San Antonio?

What are the objectives of our Data Science with Python Training in San Antonio?

Who should go for this Python Data Science online course in San Antonio?

What are the prerequisites for this Data Science with Python Training in San Antonio?

Testimonial Reviews

Video Reviews

Python Data Science Training in San Antonio FAQs

What is the main purpose of using Python?

How much Python is required for data science?

Is Python enough to become a data scientist?

What is the use of data science?

Does data science have scope in the future?

What is the role of a data scientist?

What is the cycle of data science?

What are the five steps of the data science process?

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