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    Data Science with Python Certification Course

    BY
    Edureka

    Join the Data Science with Python Certification Course to understand machine learning’s mechanism and its implementation in Python.

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    Mode

    Online

    Duration

    6 Weeks

    Fees

    ₹ 21995

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based
    Frequency of Classes Weekends

    Course overview

    The Data Science with Python Certification programme enables candidates to become skilled data scientists. Hence, the curriculum will teach you all the necessary concepts of Time series, statistics, and ML algorithms such as the unsupervised, supervised, and reinforcement. You will also gain fluency in other ML algorithms like clustering, regression, Random Forest, Decision Trees, Q-Learning, and Naïve Bayes. Moreover, you will experience the industry-relevant practices by solving real-time case studies on aviation, HR, social media, and healthcare.

    Edureka’s Data Science with Python Certification Course offers a feature-rich curriculum. You will attend instructor-led live classes, work on frequent assignments, have a lifetime LMS access, and engage in peer interaction via the community forum. Besides, you will also receive lifetime access to the 24x7 support team to resolve technical queries.

    Moreover, the online Data Science with Python Certification Course will award you with a prestigious certificate, post-completion. You will also receive a ‘resume-building tool’ to create a professional CV and ace recruitment for top-tier companies like Dell, Honeywell, Cisco, VMware, etc.

    The highlights

    • Instructor-led sessions
    • Self-paced learning
    • Pre-recorded videos
    • Placement assistance
    • Lifetime access to LMS and 24x7 support
    • Assignments after every class
    • Real-life projects
    • Case studies
    • Community forum
    • Trained instructors
    • Course certificate 
    • Hands-on projects

    Program offerings

    • Lifetime lms access
    • 24x7 online support
    • Instructor-led classes
    • Resume building
    • Community forum
    • Industry-relevant projects
    • Case studies
    • Course certificate

    Course and certificate fees

    Fees information
    ₹ 21,995

    The fees for the course Data Science with Python Certification Course is -

    Fees componentsAmount 
    Original priceRs. 21,995
    Discounted priceRs. 19,795

      *No Cost EMI starts at Rs. 6,599 / month

    certificate availability

    Yes

    certificate providing authority

    Edureka

    Eligibility criteria

    Eligibility Criteria

    Candidates with a Python development experience are eligible to join the Data Science with Python Certification Course by Edureka. While Python experience is more advantageous, Data analysis fundamentals practised over SAS/R or other tools is a plus. Additionally, Edureka will provide you with a complimentary programme, i.e. the “Python Statistics for Data Science”, as a self-paced training. You will receive this post-enrollment.

    What you will learn

    Machine learning Data science knowledge Knowledge of python

    Completing the Data Science with Python Certification Course by Edureka will make you adept in these concepts: -

    • Fundamentals and mechanisms of Machine Learning
    • Implementing Machine Learning in Python 
    • Data analysis automation via Python
    • A Machine Learning engineer’s roles
    • Working with real-time data
    • Discussing ML algorithms and their implementations
    • Learning the techniques and tools for predictive modelling
    • Knowledge about Time Series as well as it’s related concepts
    • Validating ML algorithms
    • Living in the present, handling the future business

    The syllabus

    Module 1 - Introduction to Python for Data Science

    Topics
    • Python scripting
    • Variables & types
    • Conditions & loops
    • Function basics
    • Lambda usage
    • Lists & tuples
    • Dictionaries
    • File reading
    • Error handling
    • Jupyter setup
    Hands-on
    • Writing a “Hello World” script
    • Manipulating lists and dictionaries
    • Reading a CSV file
    Skills
    • Core Python programming
    • Data science environment setup

    Module 2 - Working with Python Programming

    Topics
    • Set operations
    • List comprehensions
    • Generator functions
    • Using modules
    • Regex patterns
    • Special collections
    • Map & filter
    • Custom exceptions
    Hands-on
    • Using regex for data cleaning
    • Creating a generator
    • Building a custom module
    Skills
    • Intermediate Python techniques
    • Efficient code structures

    Module 3 - Advanced Python Programming for Data Science

    Topics
    • OOP concepts
    • Class inheritance
    • Context managers
    • Unit testing
    • API requests
    • Code profiling
    • Logging basics
    • JSON handling
    • Project packaging
    • Type hints
    Hands-on
    • Building a preprocessing class
    • Fetching API data
    • Writing a unit test
    Skills
    • Advanced Python programming
    • Modular code development

    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
    • NumPy array calculations
    • Cleaning data with Pandas
    • Creating a pivot table
    Skills
    • Data manipulation
    • Basic statistical analysis

    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
    • Creating Seaborn plots
    • Scraping website data
    • Normalizing a dataset
    Skills
    • Data visualization
    • Data preprocessing

    Module 6 - Statistical Methods for Data Science

    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
    Hands-on
    • Conducting a t-test
    • Visualizing correlations
    • Detecting outliers
    Skills
    • Statistical analysis
    • Result interpretation

    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
    • Setting up an ML project
    • Exploring a dataset
    Skills
    • ML workflows
    • Industry trends

    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
    • Building a linear regression model
    • Evaluating with RMSE
    Skills
    • Regression modeling
    • Model evaluation

    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
    • Logistic regression model
    • Decision tree visualization
    Skills
    • Binary classification
    • Evaluation metrics

    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
    • Random Forest model
    • Using SHAP for insights
    • Building Apriori rules
    Skills
    • Advanced classification
    • Interpretability, recommendations

    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
    • K-Means clustering
    • Applying PCA
    • Detecting anomalies
    Skills
    • Unsupervised learning
    • Dimensionality reduction

    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
    • DataRobot model building
    • KNIME workflow creation
    • Generating synthetic data
    Skills
    • AutoML prototyping
    • No Code workflows

    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
    • Q-Learning in a game
    • OpenAI Gym experiment
    Skills
    • RL algorithms
    • Practical applications

    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
    • ARIMA model
    • Prophet forecasting
    • Visualizing trends
    Skills
    • Time series analysis
    • Forecasting

    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
    • Training a model in SageMaker
    • Deploying with Google Cloud AI
    • Using S3 for data storage
    Skills
    • Cloud-based ML
    • Scalable model deployment

    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
    • Deploying with Flask
    • MLflow pipeline setup
    Skills
    • MLOps practices

    Admission details

    Edureka’s Data Science with Python Certification Course requires interest candidates to follow these steps for enrollment: -

    • Click on https://www.edureka.co/data-science-python-certification-course to visit the programme web page.
    • Scroll to see the “Enroll Now” option and select it.
    • Use your email ID and contact information to sign in.
    • Select your preferred batch and click on the “Proceed to Payment” button.
    • Proceed to pay via your preferred mode. 
    • Once done, you can start accessing the programme material.

    Filling the form

    Learners do not have to complete an application form to undertake the Data Science with Python Certification Course. Instead, they have to visit the course page, proceed with their phone number and email address, and select a batch. Once done, they can proceed to complete the fee payment and then, start learning.

    How it helps

    Professional roles such as a Chief Analytics Officer, Chief Data Scientist, or Data Scientist are some of the most lucrative ones in the analytics industry. Organisations strive to employ skilled employees who are experts of this domain, to accrue analytical insights and drive business strategies. Completing this course will help you do the same.

    The Data Science with Python Certification course offers real-time case studies, hands-on projects, and instructor-led live classes to help you learn all the core topics. You will also receive lifetime access to the 24x7 support and LMS, to keep learning, whenever, wherever.

    Additionally, the online Data Science with Python Certification Course by Edureka also offers an esteemed certificate upon completion. This will help you shine a light on your acquired techniques and skills in machine learning, Python programming, and analytics. As a result, you’ll be able to ace interviews and secure desired profiles in top-notch organisations.

    FAQs

    Do I need particular system requirements for this training?

    No. You will be using a Cloud LAB environment for practical work. This environment will already house the required software to execute the practicals.

    How will I work on the practicals?

    The curriculum will provide you with Jupyter Notebook, pre-installed on your Cloud LAB to work on your practicals. The access details will be on the LMS.

    Is there any way to go through a session if I miss it?

    If you miss a lecture or class, don’t fret. You can easily attend another live batch or go through pre-recorded videos.

    Does this training entail placement assistance?

    Yes; Edureka will provide you with a resume-building software. Using this, you will be able to build a strong CV and ace placements.

    Is there any way to contact Edureka?

    You can contact Edureka for clearing any query or doubt. You can reach them at 18442306365/ 9870276459, or email them at sales@edureka.co. 

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