Data Science Certification Course
Data preprocessing techniques.
Data Science Certification Course
0%
Completed
Module 1: Introduction to Data Science
Definition and scope of Data Science.
Reading
Importance and applications of Data Science.
Reading
Data Science process: from data collection to insights.
Reading
Role of a Data Scientist in various industries.
Reading
Ethical considerations in Data Science.
Reading
Tools and technologies used in Data Science.
Reading
Overview of real-world Data Science case studies.
Reading
Introduction to the course structure and expectations.
Reading
Module 2: Data Acquisition and Cleaning
Sources of data: structured, semi-structured, and unstructured.
Reading
Web scraping and APIs for data collection.
Reading
Data quality assessment and common issues.
Reading
Data preprocessing techniques.
Reading
Handling missing data and outliers.
Reading
Data transformation and normalization.
Reading
Data integration and feature engineering.
Reading
Hands-on exercises using popular data manipulation libraries.
Reading
Module 3: Exploratory Data Analysis (EDA)
Importance of EDA in understanding data.
Reading
Summary statistics and data visualization.
Reading
Distribution analysis and histogram plotting.
Reading
Correlation and scatter plots.
Reading
Box plots and violin plots.
Reading
Time series analysis.
Reading
Interactive data visualization tools.
Reading
Applying EDA to real datasets.
Reading
Module 4: Machine Learning Fundamentals
Basics of machine learning and its types.
Reading
Supervised vs. unsupervised learning.
Reading
Model representation and evaluation.
Reading
Training and testing datasets.
Reading
Cross-validation techniques.
Reading
Overfitting and underfitting.
Reading
Performance metrics: accuracy, precision, recall, F1-score, etc.
Reading
Implementing simple machine learning algorithms.
Reading
Module 5: Regression and Classification
Linear regression and its assumptions.
Reading
Polynomial regression and regularization.
Reading
Logistic regression for classification.
Reading
Decision trees and random forests.
Reading
Support Vector Machines (SVM).
Reading
Naive Bayes classifier.
Reading
Model selection and hyperparameter tuning.
Reading
Hands-on projects involving regression and classification.
Reading
Module 6: Clustering and Dimensionality Reduction
K-means and hierarchical clustering.
Reading
Clustering evaluation metrics.
Reading
Principal Component Analysis (PCA).
Reading
t-SNE (t-distributed Stochastic Neighbor Embedding).
Reading
Manifold learning techniques.
Reading
Applications of clustering and dimensionality reduction.
Reading
Visualizing high-dimensional data.
Reading
Case studies using clustering and dimensionality reduction.
Reading
Module 7: Natural Language Processing (NLP)
Introduction to NLP and its challenges.
Reading
Text preprocessing: tokenization, stemming, lemmatization.
Reading
Bag-of-words and TF-IDF representations.
Reading
Sentiment analysis and text classification.
Reading
Named Entity Recognition (NER).
Reading
Word embeddings: Word2Vec, GloVe.
Reading
Seq2Seq models and machine translation.
Reading
Hands-on NLP projects using libraries like NLTK and spaCy.
Reading
Module 8: Neural Networks and Deep Learning
Basics of artificial neural networks.
Reading
Activation functions and backpropagation.
Reading
Building deep neural networks.
Reading
Convolutional Neural Networks (CNN) for image data.
Reading
Recurrent Neural Networks (RNN) for sequential data.
Reading
Transfer learning and pre-trained models.
Reading
Regularization techniques in deep learning.
Reading
Implementing deep learning projects using TensorFlow or PyTorch.
Reading
Module 9: Big Data and Distributed Computing
Introduction to Big Data concepts.
Reading
Hadoop and MapReduce framework.
Reading
Apache Spark for large-scale data processing.
Reading
Working with distributed file systems.
Reading
Data streaming and real-time processing.
Reading
Handling big data challenges: scalability, reliability.
Reading
Cloud computing and data science.
Reading
Practical exercises with big data tools.
Reading
Module 10: Data Science Ethics and Privacy
Ethical considerations in data collection and usage.
Reading
Bias and fairness in machine learning.
Reading
Privacy issues and data anonymization.
Reading
GDPR and other data protection regulations.
Reading
Responsible AI and algorithmic transparency.
Reading
Case studies of ethical dilemmas in Data Science.
Reading
Implementing ethical practices in Data Science projects.
Reading
Class discussions on ethical challenges.
Reading
Module 11: Capstone Project
Forming teams and selecting project topics.
Reading
Project proposal and scope definition.
Reading
Data acquisition and preprocessing for the project.
Reading
Exploratory data analysis for insights.
Reading
Model selection and development.
Reading
Implementation of machine learning or deep learning techniques.
Reading
Evaluation metrics and performance analysis.
Reading
Project documentation and presentation.
Reading
Module 12: Future Trends in Data Science
Emerging trends in Data Science and AI.
Reading
Reinforcement learning and its applications.
Reading
Generative Adversarial Networks (GANs).
Reading
Explainable AI and model interpretability.
Reading
Quantum computing and its impact on Data Science.
Reading
AI ethics and regulation advancements.
Reading
Industry-specific applications and case studies.
Reading
Preparing for a career in Data Science: job roles, skills, and certifications.
Reading
Download Curriculum
More
Announcements
Course Info
Scroll to Top
Scan the code
Ascent
Hello 👋
Can we help you?
Whatsapp
Finish Course Early?
You have not completed all required lessons and assessments.
Your Progress
0%
Go Back to Course
Complete Anyway