Data Science
Certificate :
After Completion
Start Date :
10-Jan-2025
Duration :
30 Days
Course fee :
$150
COURSE DESCRIPTION:
- The Data Science course offers an extensive introduction to the skills necessary for analyzing, visualizing, and interpreting data to support informed decision-making.
- It encompasses key methodologies including statistical analysis, machine learning, data visualization, and big data management.
- Participants will engage in practical exercises using industry-standard tools such as Python, R, and SQL, working with authentic datasets.
- Upon completion of the course, attendees will be equipped to address intricate data issues and seek positions such as data scientist, data analyst, or business intelligence expert.
CERTIFICATION:
- Certificate of Completion (provided by the course provider or educational institution).
- Industry-Recognized Certifications (to prepare for globally acknowledged credentials):
Google Data Analytics Professional Certificate - IBM Data Science Professional Certificate
Microsoft Certified: Data Analyst Associate
Cloudera Certified Associate (CCA) Data Analyst - SAS Certified Data Scientist
Specialized Skill Badges: Achieved through platforms such as Coursera, edX, or Kaggle.t.
LEARNING OUTCOMES:
By the conclusion of the course, participants will possess the skills to:
- Grasp the Fundamentals of Data Science Understand the data science lifecycle, encompassing data collection, processing, analysis, and presentation. Identify the roles and responsibilities of data scientists across various sectors.
2. Gain Technical Skills Employ programming languages such as Python or R for data manipulation and analysis. Construct SQL queries to retrieve and manage data from relational databases. Leverage tools like Jupyter Notebook, Pandas, NumPy, and Matplotlib.
3. Enhance Statistical and Mathematical Knowledge Utilize descriptive and inferential statistics to interpret data. Apply probability theory and hypothesis testing in decision-making processes. Investigate concepts in linear algebra and calculus relevant to data science.
4. Execute Data Preprocessing and Wrangling Refine and convert raw data into structured, usable formats. Effectively manage missing data, outliers, and inconsistencies. Perform exploratory data analysis (EDA) to reveal patterns and insights.
5. Excel in Data Visualization Develop impactful charts, graphs, and dashboards using tools like Tableau, Power BI, and Seaborn. Craft compelling narratives with data to convey insights to stakeholders.
6. Implement Machine Learning Techniques Comprehend and apply supervised and unsupervised learning algorithms (e.g., regression, clustering, decision trees). Assess models using performance metrics and validation methods. Explore foundational concepts in deep learning, including neural networks and frameworks like TensorFlow.
Course Curriculum
- Objective: Understand the fundamentals of data science, its applications, and the data science workflow.
- Topics: Overview of data science, data science tools (Python, R, Jupyter), data cleaning, exploratory data analysis (EDA).
- Outcome: Learners will have a solid foundation in data science principles and the key stages in a data science project.
- Objective: Learn how to clean, preprocess, and transform raw data into a usable format.
- Topics: Handling missing data, data normalization, data transformation, feature engineering, and feature selection.
- Outcome: Students will be able to prepare real-world datasets for analysis, ensuring accuracy and consistency.
- Objective: Understand statistical concepts and methods to draw insights from data and make data-driven decisions.
- Topics: Probability theory, statistical tests (t-tests, chi-squared, ANOVA), confidence intervals, p-values.
- Outcome: Learners will be able to apply statistical methods to analyze data and validate hypotheses.
- Objective: Learn about various machine learning algorithms and how to apply them to real-world problems.
- Topics: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning.
- Outcome: Students will understand and implement machine learning algorithms for predictive and classification tasks.
- Objective: Master data visualization techniques to communicate insights effectively.
- Topics: Visualization tools (Matplotlib, Seaborn, Plotly), interactive dashboards, visual storytelling, best practices for data visualization.
- Outcome: Learners will be able to present complex data insights through clear and impactful visualizations.
- Objective: Explore advanced machine learning techniques, focusing on deep learning and neural networks.
- Topics: Introduction to neural networks, CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), TensorFlow, and Keras.
- Outcome: Students will learn how to build and train deep learning models for tasks like image classification, NLP, and time-series forecasting.
- Objective: Understand how to process and analyze textual data using NLP techniques.
- Topics: Text preprocessing, tokenization, word embeddings (Word2Vec, GloVe), sentiment analysis, topic modeling.
- Outcome: Students will be able to perform NLP tasks, including text classification, sentiment analysis, and text summarization.
- Objective: Learn how to work with large-scale datasets using big data tools and frameworks.
- Topics: Hadoop, Spark, distributed computing, data lakes, NoSQL databases.
- Outcome: Learners will be able to process and analyze large datasets efficiently using big data tools.
- Objective: Learn how to evaluate, validate, and optimize machine learning models for better performance.
- Topics: Cross-validation, model evaluation metrics (accuracy, precision, recall, F1-score), hyperparameter tuning, and model selection.
- Outcome: Students will be able to select the best model for a given problem and improve its performance through optimization.
Training Features
Fundamentals of Data Science and Tools
Gain foundational knowledge of data science concepts and tools, including Python, R, and Jupyter notebooks.
Data Wrangling and Preprocessing
Learn to clean, preprocess, and transform data to ensure it is suitable for analysis and modeling.
Machine Learning and Predictive Modeling
Master various machine learning algorithms for predictive analytics, classification, and clustering tasks.
Data Visualization Techniques
Learn to create impactful visualizations to effectively communicate insights and findings from data.
Deep Learning and Advanced AI Models
Explore advanced techniques like deep learning, neural networks, and natural language processing for more complex tasks.
Big Data Processing and Cloud Integration
Learn to handle big data with tools like Hadoop and Spark, and understand how to deploy models in the cloud for scalability.
