SQL Mastery:

Advanced T-SQL (SQL Server),
PL/SQL (Oracle), or PostgreSQL;
Complex joins

Understanding Joins:

Purpose of joins in relational databases
Types of relationships: One-to-One, One-to-Many, Many-to-Many

SQL Join Types:

INNER JOIN: Most common, returns matching rows
LEFT (OUTER) JOIN: Returns all rows from left table, matches from right
RIGHT (OUTER) JOIN: Returns all rows from right table, matches from left
FULL (OUTER) JOIN: Returns all rows when there is a match in either table
SELF JOIN: Joining a table to itself
CROSS JOIN: Cartesian product

Join Conditions:

ON clause vs. USING clause
Multiple join conditions

Subqueries

Scalar subqueries: Returning a single value
Row subqueries: Returning a single row
Table subqueries: Returning a table (used with IN, EXISTS, ANY, ALL)
Correlated subqueries: Dependent on outer query

CTEs

WITH clause for readability and complex queries
Recursive CTEs for hierarchical data (e.g., organizational charts),

Window Functions

ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE()
Aggregate window functions (SUM() OVER(…), AVG() OVER(…))

Relational Database (RDBMS) Concepts:

Data, Information, Database, DBMS, RDBMS
Key concepts: Tables, Rows (Records), Columns (Fields), Primary Keys, Foreign Keys, Relationships (One-to-One, One-to-Many, Many-to-Many)
SQL standards (ANSI SQL-92, SQL:1999, SQL:2003, SQL:2008, SQL:2011, SQL:2016)

Schema design (normalization/denormalization)

Benefits of normalization (data integrity, reduced redundancy)
Denormalization strategies for performance

Indexing strategies (clustered, non-clustered)

When to use indexes, types of indexes
Impact on read/write performance
Covering indexes, composite indexes

Data Warehousing (DW) Concepts: OLAP vs. OLTP; Kimball vs. Inmon methodologies; dimensional modeling (facts, dimensions, measures, hierarchies).

Data Governance & Security: Data quality principles (accuracy, completeness, consistency), data lineage, role-based access control (RBAC).

Data Integration (ETL/ELT):

ETL Tools: Proficiency in Microsoft SSIS (SQL Server Integration Services) for package creation, deployment, and scheduling; error handling, logging.
Cloud ETL/ELT: Azure Data Factory, AWS Glue, Google Cloud Dataflow for scalable cloud-native data pipelines.
Scripting:

Python for data manipulation (Pandas, NumPy)

Introduction to NumPy:

What is NumPy? The core library for numerical operations (e.g., C-optimized arrays for speed)
NumPy arrays (ndarray): Creation, indexing, slicing, reshaping
Array operations: Element-wise operations, broadcasting rules

Advanced NumPy:

Linear algebra operations (e.g., dot product, matrix multiplication, inverse)
Statistical functions (e.g., mean, median, standard deviation on arrays)
Random number generation (e.g., np.random.rand, np.random.randint)

Introduction to Pandas:

What is Pandas? The foundational library for data structures (e.g., Series, DataFrame)
Creating DataFrames and Series: From dictionaries, lists, NumPy arrays
Importing and exporting data: pd.read_csv, pd.read_excel, df.to_csv

Data Inspection and Cleaning:

Viewing data: df.head(), df.info(), df.describe(), df.shape
Handling missing data: df.isnull(), df.dropna(), df.fillna()
Data type conversion: df.astype(), pd.to_datetime
Removing duplicates: df.drop_duplicates()

Data Selection and Filtering:

Column selection: df[‘column’], df[[‘col1’, ‘col2’]]
Row selection: df.loc, df.iloc
Conditional filtering: Boolean indexing (e.g., df[df[‘age’] > 30])

Data Transformation:

Applying functions: df.apply(), df.map(), df.transform()
Grouping and aggregation: df.groupby(), agg() (e.g., df.groupby(‘category’)[‘value’].mean())
Merging and joining DataFrames: pd.merge(), pd.concat() (e.g., inner, outer, left, right joins)
Pivoting and melting data: df.pivot_table(), pd.melt()

Automation scripts, API integrations.

Data Loading Techniques: Full load, incremental load strategies; change data capture (CDC) mechanisms.

Data Modeling & Storage:

Dimensional Modeling: Star schema, snowflake schema implementation; slowly changing dimensions (SCD Type 1, 2, 3).
Data Lake/Lakehouse Concepts: Understanding Parquet, ORC, Delta Lake formats; structured vs. unstructured data storage.
Cloud Data Warehouses: Snowflake, Azure Synapse Analytics, AWS Redshift, Google BigQuery for large-scale analytics.

Reporting Tools: Power BI Desktop and Service (DAX, M-query, direct query, import mode), Tableau Desktop and Server (calculated fields, LOD expressions), SSRS (report builder, report server deployment).
Dashboard Design: Principles of effective data visualization (Tufte, Few); UX/UI for clear insights; storytelling with data.
Ad-hoc Reporting: Empowering business users with self-service BI capabilities via tools like Power BI or Tableau.

Analytical Processing (OLAP): Building cubes with SQL Server Analysis Services (SSAS) – Tabular and Multidimensional models; MDX queries.
Data Pipeline Orchestration: Using Airflow, Azure DevOps, or similar tools for end-to-end workflow management.
Performance Tuning: Optimizing SQL queries, ETL packages, and dashboard load times; understanding query plans, indexing.
Big Data Fundamentals: Introduction to Spark, Hadoop, NoSQL databases (MongoDB, Cassandra) for handling massive datasets.
Introduction to Data Science/Machine Learning: Basic understanding of statistical analysis, predictive modeling concepts, integration with BI tools (e.g., Python scripts in Power BI).

Communication: Translating technical concepts for non-technical stakeholders; gathering requirements effectively.
Problem-Solving: Debugging complex data issues; analytical thinking to solve business challenges.
Agile Methodologies: Participating in sprint planning, daily stand-ups, backlog grooming for BI projects.
Version Control: Using Git/Azure DevOps Repos for code management and collaboration.

Case Studies: Analyzing and designing BI solutions for specific industries (e.g., retail sales, healthcare operations, financial reporting).

Capstone Project: Developing a complete BI solution from data source to interactive dashboard using acquired skills.

Project Definition:

Choosing a dataset (e.g., Kaggle, UCI ML Repository)
Defining problem statement and objectives

Data Analysis Workflow:

End-to-end application of learned skills: data loading, cleaning, EDA, visualization, feature engineering, model building, evaluation, and interpretation

Presentation and Communication:

Preparing a Jupyter Notebook report
Summarizing findings and insights
Discussing limitations and future work