Course Details

Data Science

IT


Provider Name             :    GlobalOne Services

Course Type               :    Hybrid

Duration (Hrs)             :    90

Hours/day                 :    3

Training Type             :    Hybrid

Certification               :    yes

Orginal Price               :    25000/-

Discount Price             :    20000/-

No.of. vacancies         :    300

Last date to apply     :      2024-12-01


Full Information

Course Description

The Data Science Course (with Python) is designed to provide a strong foundation in data science fundamentals, machine learning techniques, and data analysis using Python. Covering a full range of essential topics—from data preprocessing and visualization to predictive modeling and deep learning—this course enables students to harness the power of data to make data-driven decisions. Through practical exercises, hands-on projects, and real-world applications, students will learn how to use Python’s data science libraries effectively, gain experience in solving complex problems, and be equipped to enter roles in data science, analytics, and machine learning.

Topics to be covered

Module 1: Introduction to Data Science and Python
Overview of Data Science and Its Applications
Setting Up Python Environment: Anaconda, Jupyter Notebooks
Introduction to Python Basics for Data Science (Data Types, Operators, Control Flow)
Data Science Libraries in Python: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn

Module 2: Data Preprocessing and Cleaning
Importing and Handling Datasets
Data Cleaning: Handling Missing Values, Outliers, and Duplicates
Data Transformation: Scaling, Encoding, and Normalization
Feature Engineering and Dimensionality Reduction (PCA, LDA)

Module 3: Exploratory Data Analysis (EDA)
Analyzing Data Using Descriptive Statistics
Data Visualization Techniques with Matplotlib and Seaborn
Analyzing Trends, Patterns, and Correlations in Data
Interactive Visualization with Plotly and Advanced Plotting Techniques
Module 4: Statistical Analysis and Probability for Data Science
Probability Basics and Statistical Distributions
Hypothesis Testing: T-test, Chi-square Test, ANOVA
Inferential Statistics: Confidence Intervals and P-Values
Correlation, Regression Analysis, and Causation

Module 5: Machine Learning Fundamentals
Supervised Learning:
Linear Regression, Logistic Regression
Decision Trees, Random Forest, and Gradient Boosting
K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM)
Unsupervised Learning:
Clustering Techniques (K-Means, Hierarchical Clustering)
Principal Component Analysis (PCA) and Dimensionality Reduction
Model Evaluation and Performance Metrics (Accuracy, Precision, Recall, F1 Score)

Module 6: Advanced Machine Learning and Deep Learning Concepts
Neural Networks:
Basics of Neural Networks, Activation Functions
Introduction to Deep Learning Frameworks (TensorFlow, Keras)
Building and Training Neural Networks for Classification
Natural Language Processing (NLP):
Text Preprocessing, Bag of Words, and TF-IDF
Sentiment Analysis, Text Classification, and N-grams
Time Series Analysis:
Understanding Time Series Data, Moving Averages
ARIMA Models, Forecasting, and Seasonal Trends

Module 7: Model Tuning and Optimization
Hyperparameter Tuning: Grid Search and Random Search
Cross-Validation Techniques for Model Evaluation
Handling Overfitting and Underfitting in Models
Model Deployment Techniques: Saving and Loading Models with Pickle

Module 8: Data Science Project and Capstone
Designing and Executing an End-to-End Data Science Project
Data Collection, Preprocessing, Analysis, Modeling, and Evaluation
Working with Real-world Datasets and Case Studies
Presentation and Reporting of Findings

Benefits of Course

This Data Science Course (with Python) equips you with the essential skills and tools to excel in data analysis, predictive modeling, and machine learning. You’ll gain hands-on experience with Python’s data science libraries, allowing you to manipulate, analyze, and visualize data effectively. By learning advanced machine learning techniques and participating in real-world projects, you’ll be prepared to tackle data-driven challenges and pursue a rewarding career in data science, analytics, or machine learning. Additionally, this course provides the practical experience necessary to build a strong portfolio, making you a competitive candidate in today’s data-centric job market.

Pre-Requirements

None