Data Science | vinncraft

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Certified Data Science Learning

Duration | 50 Hours

A Data Science Specialist excels in analyzing and interpreting complex datasets to derive actionable insights. Key responsibilities include data collection, cleaning, and visualization; building predictive models using machine learning algorithms; and effectively communicating findings to support data-driven decision-making. This role bridges the gap between raw data and strategic solutions, driving innovation and efficiency in various industries.

Prerequisites

Candidates should have a high school diploma and basic knowledge of mathematics, statistics, or programming. Familiarity with any data analysis tools (e.g., Python, R, or SQL) is recommended but not mandatory. Proficiency in English is essential for effective communication.

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Course Objectives

The Certified Data Science Specialist program is designed to equip participants with the skills to excel in data-driven decision-making and predictive analysis. The primary objective is to provide expertise in statistical modeling, machine learning, data visualization, Python programming, and big data tools, enabling learners to derive actionable insights and solve complex business challenges effectively.

What You Will Learn

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Our curriculum offers practical knowledge and hands-on experience to master key aspects of data science. Here's what you will learn throughout the course:

Foundations of Data Science

Statistics for Data Science
Probability Concepts
Data Types and Structures
Data Cleaning and Preparation

Programming for Data Science

Python for Data Science
R Programming
SQL for Data Manipulation
Version Control (Git)

Data Visualization

Matplotlib and Seaborn (Python)
ggplot2 (R)
Tableau
Power BI

Machine Learning

Supervised Learning (Regression, Classification)
Unsupervised Learning (Clustering, Dimensionality Reduction)
Model Evaluation Metrics
Hyperparameter Tuning

Deep Learning

Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Frameworks (TensorFlow, PyTorch)

Big Data Tools

Hadoop Ecosystem
Spark for Big Data Analytics
NoSQL Databases
Distributed Computing Concepts

Natural Language Processing (NLP)

Text Preprocessing
Sentiment Analysis
Language Models (BERT, GPT)
Named Entity Recognition (NER)

Time Series Analysis

Forecasting Techniques
ARIMA and SARIMA Models
Seasonal Decomposition
Applications in Finance and Operations

Professional Development

Building a Data Science Portfolio
Networking and Industry Trends
Preparing for Interviews (Case Studies, Algorithms)
Certifications (Google Data Analytics, IBM Data Science)

Data Science Tools

Jupyter Notebooks
Anaconda
Scikit-learn
AWS and Google Cloud for Data Science

Advanced Topics

Reinforcement Learning
Graph Analytics
Recommendation Systems
Ethical AI and Bias in Data