Data Science and Machine Learning
Become an expert in Data Science and Machine Learning
This course provides a thorough introduction to the field of data science, covering essential concepts, methodologies, and practical applications. Students will gain hands-on experience with data preprocessing, exploratory data analysis, hypothesis testing, and a range of machine learning techniques including regression, classification, and clustering. The course also covers the critical final step of deploying machine learning models in real-world environments.
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30,000 Jobs
are available India alone
source: Geeksforgeeks.org 2024
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Professional Opportunities
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Data Scientist
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AI Research Scientist
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Machine Learning Engineer
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Data Engineer
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Data Analyst
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Quantitative Analyst (Quant)
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Business Intelligence (BI) Analyst
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Big Data Engineer
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Professional Opportunities
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Data Scientist
-
AI Research Scientist
-
Machine Learning Engineer
-
Data Engineer
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Data Analyst
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Quantitative Analyst (Quant)
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Business Intelligence (BI) Analyst
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Big Data Engineer
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What will you Learn
Conduct thorough exploratory data analysis.
Preprocess and transform raw data for analysis.
Perform hypothesis testing to derive insights from data.
Build, evaluate, and interpret regression and classification models.
Implement clustering algorithms to identify natural groupings in data.
Deploy machine learning models in production environments and ensure
their
scalability and reliability.
Course Curriculum
- Overview: Understand the basics of data science and its significance.
- EDA Techniques: Learn how to summarize data, identify patterns, visualize data, and detect anomalies.
- Fundamentals: Understand null and alternative hypotheses, p-values, and statistical significance.
- Testing Methods: Conduct t-tests, chi-square tests, and ANOVA to make data-driven decisions.
- Logistic Regression: Predict binary outcomes.
- Tree-Based Methods: Implement decision trees and random forests.
- SVM and Others: Understand support vector machines and evaluate classifiers with metrics such as accuracy, precision, recall, and ROC-AUC.
- Model Export: Save models in formats suitable for production.
- API Development: Create APIs to serve model predictions using frameworks like Flask or FastAPI.
- Monitoring: Implement strategies for model performance monitoring and updates.
- Scalability: Use cloud services and containerization tools (e.g., Docker, Kubernetes) to ensure scalability.
- Data Cleaning: Techniques for handling missing values, outliers, and inconsistencies.
- Data Transformation: Methods for scaling, encoding, and feature engineering to prepare data for analysis.
- Linear Regression: Model relationships between variables with linear equations.
- Advanced Techniques: Explore polynomial regression, regularization (Lasso, Ridge), and evaluate models using metrics like MSE and R-squared.
- K-Means: Partition data into K clusters based on feature similarity.
- Hierarchical Clustering: Build nested clusters.
- Density-Based Clustering: Use DBSCAN for identifying clusters of varying shapes and sizes.
- Evaluation Metrics: Use silhouette score and Davies-Bouldin index to assess cluster quality.
Tools
LIVE Projects
Students will get hands on Live Industry Projects.
Intership with Mentorship
- Guidance
- Feedback
- Knowledge Sharing
- Skill Development
Project Scope
- Capstone Project
- Real-time Data Handling
- Data Preprocessing
- Exploratory Data Analysis
- Model Development
- Model Deployment
- Evaluation and Iteration
Who can join ?
Aspiring Data Scientists
Analysts
Machine Learning Engineers
It is suitable for beginners with a basic understanding of programming and statistics, as well as professionals looking to enhance their skills in data science and machine learning.
Key Highlights
Trainer with 20+ years industry expertise
Hands-on Practical Training
Program materials
Recognized course completion certificate
Recordings available online post session
+91 8367700222
+91 8367700222
info@skillsigma.com
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