Data & AI:
In today's data-driven world, Deming's words resonate deeply- "In God we trust, all others must bring data." Efficient augmentation & intelligent decision-making in business, are all fueled by data today.
Righty said - Data is the new oil, but without the right refinery, it's useless. From data analysts uncovering trends to data scientists building predictive/foundation models, data professionals are the refiners, transforming data into value that revolutionize businesses. It's not magic, but the expertise of data professionals that unlocks data's true value for business. Let's delve into the fascinating journey of these modern-day alchemists!
Data Foundation: Understanding Building Blocks
Data Types & Structures: Understanding the different data types (numerical, text, categorical) and common data structures (tables, databases, hierarchies). Know structured & unstructured data formats and their usage.
Speak the Language of Data: Master data science terminology and understand the diverse roles and responsibilities of data professionals such as data analyst, data engineer, data scientist, etc., enabling effective communication and collaboration across diverse teams.
Data Collection & Extraction: Learn about various data collection methods like surveys, experiments, observations, web scraping, and sampling techniques. Explore sampling techniques such as random, stratified, and cluster sampling to efficiently gather representative data subsets, while ensuring data quality.
Introduction to Spreadsheets: Master spreadsheet functions for data organization, manipulation, and analysis (e.g., Excel, Google Sheets). Explore pivot tables and introductory visualization techniques with tools like Excel and simple charts to effectively plot data.
Descriptive Statistics & Data Summarization: Gain a strong foundation in descriptive statistics like mean, median, standard deviation, and variance to understand central tendency, data distributions & probability. Understanding inferential statistical methods including formulating hypotheses, choosing appropriate statistical tests and confidence intervals to make inferences.
Data Ethics and Privacy: Gain insight into ethical considerations and data privacy laws/regulations. Understanding data sensitivity and security measures like encryption, access control, and data anonymization.
Data Analysis: Transforming Data into Insights
Introduction to Business Intelligence (BI): Understand the concept of BI to formulate business problem from data. Learn to define, measure and analyze key performance indicators (KPIs) critical for any data-driven decision making in business.
Level up Statistical skills: Understand variables in statistical modeling for bivariate and multi-variate analysis. Learn to analyze relationships between variables using techniques like scatter plots correlation analysis and multi-collinearity.
Data Warehousing: Learn the fundamentals of data warehousing, including data architecture, data lakes (flexible storage for various data types), ETL processes (Extract, Transform, Load), and data integration techniques.
Exploratory Data Analysis (EDA): Build deeper analysis with data exploration uncovering hidden data patterns and gaining insights into data. Learn to identify & treat missing values and outliers in the data. Delve into methods like descriptive statistic techniques to understand data distribution focusing on data manipulation, visualization, and interpretation to generate business insight.
Python/R for data analysis: Gain hands-on experience in exploring datasets using Python or R and perform tasks such as data cleaning, data wrangling, data preparation for data analysis tasks. Leverage tools to analyze & interpret data effectively and derive meaningful insights.
Data Visualization with Storytelling: Get familiar with visualization tools, build interactive dashboards to create dynamic & insightful visual narratives that resonate with your audience, taking your data storytelling skills to the next level.
Building the Infrastructure with Tools:
Programming: Master widely used language for data analysis & machine learning - Python/R. Explore essential python libraries for data science such as Pandas, NumPy, scikit-learn, and Matplotlib.
Database: Gain Proficiency in Database Management Systems (DBMS) & different types of databases (relational, NoSQL) with tools like MS Access and SQL for efficient data storage, retrieval, and manipulation.
Data Visualization tools: Learn to create insightful and interactive visualizations using QlikView, Tableau, and Power BI to effectively communicate data insights.
Cloud technologies: Get familiar with cloud platforms like Microsoft Azure, AWS (amazon web services), GCP (goggle cloud platform), etc. that offer tools and infrastructure for data storage, analysis, and AI development.
Machine Learning (ML) & Artificial Intelligence (AI): Learning from Data
Data Validation, Transformation & Feature Engineering: Learn to identify and correct errors, inconsistencies, and missing values in datasets to ensure data quality. Explore techniques to transform data and generate features suitable for further analysis.
Data Modelling: Get familiar with the fundamental concepts and algorithms of machine learning to build predictive models. Learn the process of building and training AI models involving data preparation, model selection, training, and evaluate and compare their performance for different tasks.
Deep Dive into Machine Learning: Explore core ML algorithms like decision trees, linear regression, and support vector machines. Understand the concept of supervised and unsupervised learning in machine learning and how different algorithms learn from data to generate model output.
Introduction to Deep Learning: Understand the fundamentals of Deep Learning, a subfield of ML used for complex tasks like image recognition and natural language processing. Get introduced to neutral networks, various types of neutral network and field of applications.
NLP (Natural Language Processing): Learn techniques for analyzing and understanding text data, crucial for tasks like sentiment analysis and chatbots.
Generative AI & foundation models: Understand the architecture of foundation models and their role in Generative AI. Explore foundation models from providers - Hugging face, Azure OpenAI, Google, Meta, etc.
Case Studies & Real-World Applications: Gain hands-on experience with case studies from real-world examples showcasing the application of data analysis and exploration across diverse industries and domains.