Data Analytics in Collaboration with IBM

Module 1: Introduction to Data Analytics (Duration: 2 weeks)

Week 1: Understanding the Fundamentals

• Introduction to data analytics and its significance in various industries
• Overview of data lifecycle: acquisition, storage, processing, analysis, and visualization
• Exploring the role of data analysts in driving data-driven decision-making processes

Week 2: Ethical Considerations and Best Practices

• Understanding ethical considerations in data analytics, including privacy, security, and bias
• Compliance with data protection regulations such as GDPR and HIPAA
• Best practices for data collection, storage, and analysis to ensure integrity and transparency

Module 2: Data Acquisition and Preparation (Duration: 3 weeks)

Week 3: Introduction to SQL

• Basics of SQL (Structured Query Language) for data querying and manipulation
• Writing SQL queries to retrieve, filter, and sort data from databases
• Hands-on exercises using SQL to perform data manipulation tasks

Week 4: Data Cleaning and Preprocessing

• Techniques for data cleaning and preprocessing to ensure data quality and consistency
• Dealing with missing values, outliers, and duplicates in datasets
• Strategies for transforming raw data into usable formats for analysis

Week 5: Introduction to Python Programming

• Basics of Python programming language: syntax, data types, variables, and control structures
• Hands-on practice with Python using Jupyter Notebook or similar IDE
• Introduction to Python libraries for data analysis, including Pandas and NumPy

Module 3: Descriptive Statistics and Exploratory Data Analysis (EDA) (Duration: 4 weeks)

Week 6: Fundamentals of Descriptive Statistics

• Understanding measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation)
• Calculation and interpretation of descriptive statistics using Python and Excel
• Visualizing data distributions using histograms, box plots, and scatter plots

Week 7: Exploratory Data Analysis Techniques

• Exploring relationships between variables using correlation analysis
• Detecting outliers and anomalies in datasets using statistical methods
• Hands-on practice with EDA techniques using Python libraries such as Matplotlib and Seaborn

Week 8: Statistical Modeling and Inference

• Introduction to statistical modeling techniques, including linear regression and logistic regression
• Hypothesis testing for making inferences from data and evaluating statistical significance
• Model validation methods and interpretation of results using Python and statistical software

Week 9: Machine Learning Fundamentals

• Overview of machine learning concepts and algorithms, including supervised and unsupervised learning
• Hands-on experience with building and evaluating machine learning models using Python libraries such as scikit-learn
• Understanding the strengths and limitations of different machine learning algorithms

Module 4: Data Visualization and Dashboarding (Duration: 3 weeks)

Week 10: Principles of Data Visualization

• Understanding the principles of effective data visualization and dashboard design
• Choosing the right visualization techniques for different types of data and analysis tasks
• Designing visually compelling charts, graphs, and dashboards using tools like Tableau and PowerBI

Week 11: Advanced Data Visualization Techniques

• Creating interactive and dynamic visualizations to explore large datasets
• Customizing visualizations with advanced formatting options and design elements
• Incorporating storytelling techniques to communicate insights and findings effectively

Week 12: Capstone Project and Career Development (Duration: 2 weeks)

Week 12: Capstone Project

• Applying learned concepts and techniques to solve a real-world data problem
• Developing a comprehensive data analysis project from data acquisition to visualization
• Presenting findings and insights effectively to stakeholders and peers

Week 13: Certification and Career Support

• Obtaining IBM and IAQMC certification in Data Analytics upon successful completion
• Crafting a professional portfolio to showcase skills and projects to potential employers
• Resume building and optimizing online professional profiles for job search success
• Networking strategies for connecting with industry professionals and accessing job opportunities
• Interview preparation and mock interviews to build confidence and readiness for job interviews

Conclusion: This comprehensive 12-week data analytics bootcamp, in collaboration with IBM and IAQMC, covers essential concepts, tools, and techniques required to excel in the field of data analytics. With personalized career guidance and guaranteed placement assistance, graduates will be well-prepared to embark on a successful career as data analysts in various industries.

Hashtags: #DataAnalytics #DataScience #IBM #IAQMC #DataAnalysis #DataVisualization #MachineLearning #CareerDevelopment #PythonProgramming #SQL #BigData #Certification #JobPlacement #DataDrivenDecisionMaking