Data Science with Python Certification Course

BY
Edureka

Develop your career in data science by enrolling for the Data Science with Python Certification Course by Edureka.

Mode

Online

Duration

6 Weeks

Fees

₹ 18695 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

Data Science with Python Certification Course is being provided by Edureka for the learners who are keen to strengthen their base of data science concepts. Edureka is an educational platform, which provides the learners with a chance to pursue their courses through live online courses.

The participants of this course will not only learn the fundamentals of Statistics, Python and Machine Learning but will also understand the application of python in Data Science. This course is a step by step guide to Data science and python with extensive hands on.

They will learn from the basics of Statistics such as median, mean, and mode to exploring features such as Regression, Data Analysis, classification, clustering, cross validation, naive Bayes, label encoding, random forests, support vector machines and decision trees with examples and exercises to help the participants understand better.

They will be taught reinforcement learning, an important aspect of Artificial Intelligence and application of Machine Learning Algorithms. This course will cover basic as well as advanced concepts of Python like writing Python scripts, file operations, the sequence in Python and the usage of libraries like pandas and Numpy.

The highlights

  • Live sessions by instructors
  • Course certificate
  • 24 x 7 Expert Support
  • Course access for lifetime

Program offerings

  • Assignments
  • Community forums
  • Real life case studies

Course and certificate fees

Fees information
₹ 18,695  ₹21,995

Fee details for Data Science with Python Certification Course

HeadAmount in INR
Original priceRs. 21,995
Discounted priceRs. 19,795

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

certificate availability

Yes

certificate providing authority

Edureka

Who it is for

The Data Science with Python Certification Course is suitable for:

  • Programmers
  • Developers
  • Technical Leads
  • Architects
  • Developers aspiring to be a ‘Machine Learning Engineer'
  • Analytics Managers leading a team of analysts
  • Business Analysts who wish to understand Machine Learning (ML) Techniques
  • Information Architects who aspire to gain expertise in Predictive Analytics, 'Python' professionals keen on designing automatic predictive models.

Eligibility criteria

Education

Candidates interested in pursuing this course should have a basic understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be beneficial. However, you will be given “Python Statistics for Data Science” as a self-paced course once they register for the course.

Certification Qualifying Details

Edureka shall provide a course certificate for Data Science with Python Certification Course to those participants who successfully complete the final project. 

What you will learn

Knowledge of python

As the Data Science with Python Certification Course comes to an end, participants will have gained knowledge about the following:

  • Learn the fundamentals of Python
  • Understand how to create generic python scripts
  • Acknowledge the concept of Machine Learning and types of machine learning
  • Grasp the Basic Functionalities of a data object
  • Understand the Supervised Learning Techniques and their implementation
  • Expertise in performing factor analysis using PCA
  • Learn about the various types of clustering used for analyzing the data
  • Understand the Association rules and their extensions

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


Filling the form

Interested candidates can observe the following steps to enrol for the Data Science with Python Certification Course:

Step 1: Click on the given official URL for the course https://www.edureka.co/data-science-python-certification-course

Step 2: Candidates have to select enrol now.

Step 3: Fill in the details asked for and select start learning

Step 4: Candidates are required to select their preferred batch before they can proceed for payment.

Step 5: Pay the course fees through the preferred method to complete the course enrollment.

How it helps

The Data Science with Python Certification Course will enable the participants to gain a detailed understanding of the concepts of data science using python. They will be skilled enough to build and use Machine learning applications and other scientific computations using python.

Python is becoming an important element for Data Analytics and a must for Professionals in the Data Analytics domain. Online learning mode will make it feasible for the participants to learn at their own pace. Students/ professionals can complete the course while meeting their time constraints.

This course will enable the participants to excel and further their career in this domain of the industry. It will not only develop their skills but also their resume. The course offerings will equip the participants with industry relevant skills which will in turn accelerate the career growth of the participants.

Real life case studies will provide the participants with hands-on experience. They will be given conceptual as well as practical knowledge which will make them industry ready. With their skills and knowledge, participants can apply for higher profile jobs at companies hiring certified professionals for data analytics. Participants of this course are likely to get an upper hand over other professionals of this domain.

FAQs

What if the participants are unable to attend any class?

If the participants are unable to attend a class, they have the option of attending the missed lecture through another batch or they can attend the recorded class that shall be available in their LMS.

Will Edureka provide placement assistance?

Participants will be given a resume builder tool in their LMS through which they will be able to create a good resume in 3 easy steps. They will be given unlimited access to templates for different roles and designations.

Can the participants attend a demo lecture?

Participants can view the sample class recording. It will give them a clear insight as to how the classes will be conducted, quality of instructors and the level of interaction in the classes.

Who will be the course instructors?

All the Edureka instructors are professionals from the Industry and subject matter experts. They have a minimum of 10-12 years of relevant IT experience. They are given training by Edureka to educate the participants better.

What if the participants have more queries?

If the participants have any queries, they can contact the officials at +91 98702 76459/1844 230 6365 (US Toll-Free Number) or drop a mail at sales@edureka.co

How will the practicals be executed?

Participants can do their assignments/case studies using Jupyter Notebook. It will be installed on their Cloud Lab environment, access details of which will be given on their LMS. They will be able to access their Cloud Lab environment from a browser.

What is the purpose of CloudLab?

CloudLab is a pre-installed cloud-based Jupyter Notebook with Python packages on the cloud-lab environment. It is provided by Edureka as a part of the Python Certification Course in which the participants can execute in-class demos and work on real-life projects.

How many case studies are included in the course?

This course includes 40 case studies which will enhance the learning experience of the participants. They will also be given 4 Projects to improve their implementation skills.

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