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Data Science with Python Certification Course
Data Science with Python Certification Course
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Live Online Classes starting on 16th Aug 2025
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Why enroll for Data Science with Python Certification Course?
Data Science with Python Certification Course Benefits
Data Science with Python training covers industry-relevant skills for the fast-growing worldwide job market. The worldwide datasphere is expected to reach 181 zettabytes by 2025, driving the need for Python experts. Data science jobs are predicted to grow by 35–36%, creating 21,000 new positions annually, making this training a gateway to high-value, future-proof careers.
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Why Data Science with Python Certification Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
About your Data Science with Python Certification Course
Data Science with Python Skills Covered
Data Science with Python Tools Covered
Data Science with Python Course Syllabus
Curriculum Designed by Experts
Introduction to Python for Data Science
10 Topics
Topics
- Python scripting
- Variables & types
- Conditions & loops
- Function basics
- Lambda usage
- Lists & tuples
- Dictionaries
- File reading
- Error handling
- Jupyter setup
![skill]( "Hands On Experience")
- Writing a “Hello World” script
- Manipulating lists and dictionaries
- Reading a CSV file
![skill]( "skill you will learn")
- Core Python programming
- Data science environment setup
Working with Python Programming
8 Topics
Topics
- Set operations
- List comprehensions
- Generator functions
- Using modules
- Regex patterns
- Special collections
- Map & filter
- Custom exceptions
![skill]( "Hands On Experience")
- Using regex for data cleaning
- Creating a generator
- Building a custom module
![skill]( "skill you will learn")
- Intermediate Python techniques
- Efficient code structures
Advanced Python Programming for Data Science
10 Topics
Topics
- OOP concepts
- Class inheritance
- Context managers
- Unit testing
- API requests
- Code profiling
- Logging basics
- JSON handling
- Project packaging
- Type hints
![skill]( "Hands On Experience")
- Building a preprocessing class
- Fetching API data
- Writing a unit test
![skill]( "skill you will learn")
- Advanced Python programming
- Modular code development
Data Analysis with NumPy and Pandas
10 Topics
Topics
- NumPy arrays
- Vector operations
- Math functions
- Series handling
- DataFrames
- Dataset merging
- Missing values
- Pivot tables
- Data summaries
- Memory tuning
![skill]( "Hands On Experience")
- NumPy array calculations
- Cleaning data with Pandas
- Creating a pivot table
![skill]( "skill you will learn")
- Data manipulation
- Basic statistical analysis
Data Visualization and Preprocessing Techniques
9 Topics
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
![skill]( "Hands On Experience")
- Creating Seaborn plots
- Scraping website data
- Normalizing a dataset
![skill]( "skill you will learn")
- Data visualization
- Data preprocessing
Statistical Methods for Data Science
10 Topics
Topics
- Descriptive stats
- Variance, standard deviation
- Probability
- Normal distribution
- Hypothesis testing: t-tests
- Correlation: Pearson coefficient
- Outlier detection: z-score
- Sampling: random sampling
- Statistical visualization
- P-values: significance
![skill]( "Hands On Experience")
- Conducting a t-test
- Visualizing correlations
- Detecting outliers
![skill]( "skill you will learn")
- Statistical analysis
- Result interpretation
Fundamentals of Machine Learning
9 Topics
Topics
- CRISP-DM process
- ML categories
- Python for ML
- ML tools
- Data lifecycle
- Evaluation
- Feature basics
- AI ethics
- Industry insights
![skill]( "Hands On Experience")
- Setting up an ML project
- Exploring a dataset
![skill]( "skill you will learn")
- ML workflows
- Industry trends
Supervised Learning – Regression Analysis
10 Topics
Topics
- Linear regression
- Gradient descent
- Polynomial regression
- Ridge regression
- Error metrics
- R-squared
- Cross-validation
- Residual analysis
- Feature selection
- Overfitting mitigation
![skill]( "Hands On Experience")
- Building a linear regression model
- Evaluating with RMSE
![skill]( "skill you will learn")
- Regression modeling
- Model evaluation
Supervised Learning – Classification Fundamentals
10 Topics
Topics
- Logistic regression
- Binary labels
- Decision trees
- Confusion matrix
- Precision & recall
- ROC curve
- Overfitting
- Feature ranking
- Model validation
- Class imbalance
![skill]( "Hands On Experience")
- Logistic regression model
- Decision tree visualization
![skill]( "skill you will learn")
- Binary classification
- Evaluation metrics
Supervised Learning – Advanced Classification
11 Topics
Topics
- Random forests
- SVM
- XGBoost
- Grid search
- Random search
- SHAP values
- SMOTE
- Model stacking
- Association rules
- Recommendation engines
- Model evaluation
![skill]( "Hands On Experience")
- Random Forest model
- Using SHAP for insights
- Building Apriori rules
![skill]( "skill you will learn")
- Advanced classification
- Interpretability, recommendations
Unsupervised Learning and Clustering Techniques
9 Topics
Topics
- K-Means clusters
- Elbow method
- Hierarchical clustering
- DBSCAN logic
- PCA reduction
- Anomaly detection
- Silhouette score
- Segmentation use
- Cluster visuals
![skill]( "Hands On Experience")
- K-Means clustering
- Applying PCA
- Detecting anomalies
![skill]( "skill you will learn")
- Unsupervised learning
- Dimensionality reduction
AutoML and No-Code Data Science Solutions
7 Topics
Topics
- AutoML tools
- DataRobot
- KNIME workflows
- H2O.ai models
- Synthetic data
- Rapid prototyping
- AI fairness
![skill]( "Hands On Experience")
- DataRobot model building
- KNIME workflow creation
- Generating synthetic data
![skill]( "skill you will learn")
- AutoML prototyping
- No Code workflows
Reinforcement Learning Essentials (Self-paced)
10 Topics
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
![skill]( "Hands On Experience")
- Q-Learning in a game
- OpenAI Gym experiment
![skill]( "skill you will learn")
- RL algorithms
- Practical applications
Time Series Analysis and Forecasting Methods (Self-paced)
10 Topics
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
![skill]( "Hands On Experience")
- ARIMA model
- Prophet forecasting
- Visualizing trends
![skill]( "skill you will learn")
- Time series analysis
- Forecasting
Machine Learning on Cloud Platforms (Self-paced)
8 Topics
Topics
- Cloud ML Introduction
- AWS SageMaker
- Google Cloud AI
- Azure ML
- Cloud storage
- Serverless ML
- Model deployment
- Scalability
![skill]( "Hands On Experience")
- Training a model in SageMaker
- Deploying with Google Cloud AI
- Using S3 for data storage
![skill]( "skill you will learn")
- Cloud-based ML
- Scalable model deployment
MLOps Fundamentals (Self-paced)
7 Topics
Topics
- MLOps Introduction
- CI/CD for ML
- Flask API Deployment
- MLflow Model Tracking
- Docker Containerization
- Model Drift Monitoring
- Model Lifecycle Management
![skill]( "Hands On Experience")
- Deploying with Flask
- MLflow pipeline setup
![skill]( "skill you will learn")
- MLOps practices
Data Science with Python Course Description
About Data Science with Python Certification Course
Our Data Science with Python Certification Course gives you all the skills you need, from basic Python programming and data processing with NumPy and Pandas to making detailed visualizations with Matplotlib and Seaborn. You will learn how to do statistical analysis, test hypotheses, and preprocess data. Then you will develop and test machine learning models (regression, classification, clustering) using projects that are like real business situations.
You can improve your skills even more by looking into AutoML platforms like H2O.ai and DataRobot. You'll have lifetime access to the materials, community support, and a recognized certification.
Why Learn Data Science using Python?
Python is popular for learning data science because of its clean, accessible syntax, which allows you to focus on analysis rather than boilerplate coding. With an extensive ecosystem that includes NumPy for rapid arrays, Pandas for DataFrames, Matplotlib/Seaborn for graphs, scikit-learn for machine learning, and TensorFlow/PyTorch for deep learning, there's little need to recreate everything from scratch.
Python operates on Windows, macOS, and Linux, connects seamlessly with databases, Excel, business intelligence tools, and cloud platforms, and is scalable from simple scripts to production pipelines. Finally, a large, engaged community ensures ongoing library enhancements and an abundance of learning resources.
Why do we need Python for data science?
One of the primary advantages of Python is its straightforward syntax and intelligibility. It reduces the time that data analysts would otherwise spend acquainting themselves with a programming language.
Why do we need to learn data science?
Data science is a modern field that continues to affect how industries behave. Learning this skill may help you keep up with trends in the industry you work in and provide important insights.
Why learn Machine Learning using Python?
Data science is a set of techniques that enables computers to learn the desired behavior from data without being explicitly programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This certification training exposes you to different classes of machine learning algorithms, like supervised, unsupervised, and reinforcement learning algorithms.
This Data Science with Python Training imparts the necessary skills like data pre-processing, dimensional reduction, and model evaluation, and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forests, Naive Bayes, and many more.
What are the objectives of our Data Science with Python Training?
The primary goals of our Python-based Data Science training programme are to:
- Enable learners to write clear, efficient code for any data task by providing them with a \ comprehensive understanding of core and advanced Python skills.
- Educate learners about the practical application of NumPy and Pandas for the purpose of cleansing, transforming, and summarizing datasets, as well as Matplotlib/Seaborn for the purpose of generating visually appealing charts and dashboards.
- Build and evaluate regression, classification, clustering, and recommendation models with scikit-learn (and deep learning frameworks), and perform hypothesis testing.
- Interpret metrics such as RMSE, F1, and ROC.
- Deploy models on cloud, implement CI/CD pipelines, containerization (Docker), and model monitoring (MLflow) to ensure production readiness.
- Provide End-to-End Data Solutions Guide learners through real-world projects.
Who should go for this Python Data Science and ML online course?
- 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?
The prerequisites for Edureka's Python Data Science and ML course training include the fundamental understanding of Computer Programming Languages.
How will I execute the practicals in this online Python course?
You will do your assignments and case studies using Jupyter Notebook, which is already installed on your Cloud LAB environment (access it from a browser). The access credentials are available on your LMS. Should you have any queries, the 24*7 Support Team will promptly assist you.
Data Science with Python Course Projects
Domain: Retail
A big retail chain keeps track of what customers buy, how often they buy it, and their demographics, such as their age and where they live. The marketing team wants to put custom....Domain: BFSI
A regional bank sees that more and more clients are closing their accounts. The bank aims to guess which customers are most likely to depart based on things like their account ba....Domain: Real Estate
A real estate agency wishes to give people who are looking to buy a property realistic pricing projections. You need to make a regression model that can estimate house values bas....Domain: Aviation
An airline has to predict how many passengers it will have each month in order to make the best use of its flight schedules, crew assignments, and fuel planning. You need to use ....Domain: Finance
A credit card firm loses money because of fake transactions. They want a system that can find strange things in transaction data, including spending patterns that don't make sens....Domain: Manufacturing
A manufacturing plant uses heavy machinery that sometimes breaks down, which costs a lot of time and money. The plant wants to know when equipment is likely to break down by look....Domain: Finance
An investment company wants to use machine learning to guess the stock prices of a certain company so they can make better trading choices. You need to use past stock prices and ....Domain: Supply Chain/Retail
A retail company is having trouble keeping its inventory levels balanced, which means it either runs out of stock (losing revenues) or has too much stock (raising costs). You nee....Data Science with Python Certification
To unlock the Edureka’s Data Science with Python Training course completion certificate, you must ensure the following:
- Completely participate in this Edureka’s Data Science with Python Training Course.
- Evaluation and completion of the quizzes and projects listed.
Yes, Data Scientist is a good career option for those interested in working with data and extracting insights from it. With the explosive growth of data in recent years, the demand for skilled data scientists has increased significantly. As a Data Scientist, one can work in a variety of industries such as healthcare, finance, marketing, and more. The job typically requires a strong foundation in statistics, machine learning, and programming skills, as well as a good understanding of business and domain knowledge. Data Scientist is responsible for collecting, analyzing, and interpreting large and complex data sets to inform business decisions and strategies. Overall, data science is a challenging and rewarding career option with a promising outlook for the future.
Yes, Machine Learning Engineer is a good career option for those interested in working with machine learning algorithms and implementing them in real-world applications. Machine learning is a rapidly growing field with increasing demand for professionals who can build and deploy machine learning models to automate tasks and extract insights from large amounts of data. As a Machine Learning Engineer, one can work in a variety of industries such as healthcare, finance, e-commerce, and more. The job typically requires a strong foundation in machine learning, programming skills, and a good understanding of software engineering principles. Overall, machine learning engineering can be a challenging and rewarding career option with a promising outlook for the future.
To learn data science and machine learning as a beginner, one can start by learning Python programming and then move on to data analysis. After understanding data analysis, one can learn the basics of machine learning, and apply machine learning algorithms to real-world problems. Edureka’s Data Science with Python Certification Training provides a structured learning experience that helps beginners gain practical experience and develop the skills necessary to become proficient in data science and machine learning.
Data Science with Python Certification provides a strong foundation in data science, machine learning, and Python programming. This certification is valuable for several reasons:
- Demonstrates Mastery of Key Skills: Certification indicates that an individual has a strong understanding of data science concepts, machine learning techniques, and Python programming skills.
- Improves Job Prospects: Data science and machine learning are high-growth industries, and certification can improve job prospects by demonstrating expertise in these areas.
- Increases Earning Potential: Certified data scientists and machine learning engineers often earn higher salaries compared to their non-certified peers.
- Enhances Credibility: Certification is a recognized indicator of expertise and can enhance an individual's credibility in the field.
- Keeps Skills Up-to-Date: Data science and machine learning are constantly evolving fields, and certification requires individuals to stay up-to-date with the latest technologies and techniques.
- Enables Career Advancement: Certification can enable individuals to advance their careers by demonstrating mastery of key skills and increasing their value to their organization.
Data Science with Python Certification can open up various job roles in the field of data science and machine learning. Some of the common job roles available after completing this certification include:
- Data Analyst: A data analyst collects, analyzes, and interprets large datasets to help businesses make informed decisions.
- Machine Learning Engineer: A Machine Learning Engineer is responsible for designing, building, and deploying machine learning models that can automate certain tasks.
- Data Scientist: A Data Scientist is responsible for analyzing and interpreting complex data to extract insights and build predictive models.
- Business Intelligence Analyst: A Business Intelligence Analyst is responsible for analyzing data to provide insights that can help businesses make informed decisions.
- AI Architect: An AI Architect is responsible for designing and implementing AI systems, including machine learning algorithms and neural networks.
- Research Scientist: A Research Scientist is responsible for conducting research and experiments to develop new machine learning algorithms and techniques.
You do not need a coding background to enroll in this Data Science with Python course. The course begins with basic modules in which we cover the fundamentals of Python coding. In fact, you do not need prior knowledge in data science or machine learning either. All relevant topics are a part of this course from scratch.
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I recently completed the Data Science with Python course from Edureka, and Iâm excited to share my experience. As someone who wanted to delve deeply into data science, I found this course to be exceptional in both content and delivery. **Course Curriculum:** The curriculum of the Edureka Data Science with Python course is comprehensive and meticulously structured. It covers a wide range of topics essential for a data scientist, including data manipulation, statistical analysis, machine learning algorithms, and data visualization. What stood out to me was how the course balanced theoretical concepts with practical application. Each module built upon the previous one, making complex topics more digestible and easier to understand. **Teaching Standards:** The quality of instruction was top-notch. The instructors demonstrated a deep understanding of the subject matter and presented the content in a clear, engaging manner. Their explanations were thorough, and they were always ready to address any questions or concerns. The interactive nature of the live sessions, combined with the practical exercises, significantly enhanced the learning experience. The support provided by Edureka was also commendable, with prompt assistance from the technical support team whenever needed. **Course Materials:** The course materials were incredibly well-organized and useful. Edureka provided a wealth of resources, including detailed lecture notes, coding assignments, and a range of practical exercises. The supplementary materials, such as recommended readings and tutorials, were also beneficial for deepening my understanding of various topics. **Real-World Case Studies:** One of the highlights of the course was the inclusion of real-world case studies. These case studies were both challenging and rewarding. They provided an opportunity to apply theoretical knowledge to practical problems, helping to bridge the gap between academic learning and real-world application. The case studies we
September 03, 2024
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Data Science with Python Course FAQs
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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