Data Analytics Roadmap

1. Fundamentals of Statistics and Mathematics
  - Understand denoscriptive statistics: mean, median, mode, variance, standard deviation.
  - Basics of probability theory.
  - Hypothesis testing and statistical inference.
  - Some linear algebra and calculus basics (optional depending on needs).

2. Learn Excel and Google Sheets
  - Master spreadsheet basics: formulas, functions, pivot tables.
  - Data visualization with charts and graphs.
  - Basic automation with macros and advanced formulas.

3. Programming for Data Analytics
  - Choose Python or R as your main analytical programming language.
  - Python libraries: pandas (data manipulation), numpy (numerical operations), matplotlib and seaborn (visualization).
  - For R: dplyr, ggplot2.
  - Use Jupyter Notebook (Python) or RStudio for coding environment.

4. Databases and SQL
  - Understand relational databases and how data is stored.
  - Learn SQL queries: SELECT, JOIN, GROUP BY, aggregation functions.
  - Practice querying real databases.

5. Data Visualization Tools
  - Learn tools like Tableau, Power BI, or Looker.
  - Build interactive dashboards and reports.
  - Understand best practices for effective visualization (color, simplicity, clarity).

6. Business Analytics Fundamentals
  - Understand business processes and workflows.
  - Define Key Performance Indicators (KPIs).
  - Translate business questions into analytical problems.

7. Data Cleaning and Preprocessing
  - Handle missing, inconsistent, and outlier data.
  - Data transformation and normalization techniques.
  - Use Python (pandas) or other tools to clean data effectively.

8. Basics of Machine Learning (Optional for Advanced Skills)
  - Understand simple models: linear regression, classification.
  - Use scikit-learn library in Python.
  - Apply models for forecasting and clustering.

9. Hands-on Practice and Projects
  - Work on real datasets from Kaggle or other platforms.
  - Build a portfolio showcasing your data analysis projects.
  - Participate in data competitions and hackathons.

10. Communication and Reporting
  - Develop skills in presenting data insights clearly.
  - Create compelling reports and presentations.
  - Learn to work with stakeholders to tailor insights.

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Key data science programming languages and tools

Data Science Roadmap

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There are several AI tools and libraries available to assist with coding in Python. Here are some of the most popular ones:

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2. Tabnine: An AI code completion tool that supports various IDEs and code editors. It uses deep learning models to predict and suggest code completions.

3. Kite: An AI-powered code completion and documentation tool that integrates with many popular IDEs. It offers in-line code completions and documentation for Python.

4. PyCharm's Code Completion: JetBrains' PyCharm IDE comes with advanced code completion features, which are enhanced by AI to provide context-aware suggestions.

5. Jupyter Notebooks with AI Integration: Jupyter notebooks can integrate with various AI tools and libraries for code completion and suggestions, like JupyterLab Code Formatter or extensions that integrate with AI models.

6. DeepCode: An AI-based code review tool that helps identify and fix bugs, security vulnerabilities, and code quality issues.

7. IntelliCode: An extension for Visual Studio Code that uses AI to provide code suggestions and improve productivity.

8. Codota: An AI-powered code suggestion tool that integrates with many IDEs and provides context-aware code completions.

9. Repl.it Ghostwriter: An AI-powered code completion tool available in the Repl.it online coding environment.

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Machine Learning Roadmap

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A power-packed selection of 100% free, certified courses from top institutions:

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- Don’t just report metrics—ask what story they’re telling and what recommendation can follow.

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Complete Syllabus for Data Analytics interview:

SQL:
1. Basic
  - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
  - Basic JOINS (INNER, LEFT, RIGHT, FULL)
  - Creating and using simple databases and tables

2. Intermediate
  - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
  - Subqueries and nested queries
  - Common Table Expressions (WITH clause)
  - CASE statements for conditional logic in queries

3. Advanced
  - Advanced JOIN techniques (self-join, non-equi join)
  - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
  - optimization with indexing
  - Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Basic
  - Syntax, variables, data types (integers, floats, strings, booleans)
  - Control structures (if-else, for and while loops)
  - Basic data structures (lists, dictionaries, sets, tuples)
  - Functions, lambda functions, error handling (try-except)
  - Modules and packages

2. Pandas & Numpy
  - Creating and manipulating DataFrames and Series
  - Indexing, selecting, and filtering data
  - Handling missing data (fillna, dropna)
  - Data aggregation with groupby, summarizing data
  - Merging, joining, and concatenating datasets

3. Basic Visualization
  - Basic plotting with Matplotlib (line plots, bar plots, histograms)
  - Visualization with Seaborn (scatter plots, box plots, pair plots)
  - Customizing plots (sizes, labels, legends, color palettes)
  - Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Basic
  - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
  - Introduction to charts and basic data visualization
  - Data sorting and filtering
  - Conditional formatting

2. Intermediate
  - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
  - PivotTables and PivotCharts for summarizing data
  - Data validation tools
  - What-if analysis tools (Data Tables, Goal Seek)

3. Advanced
  - Array formulas and advanced functions
  - Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
  - Dynamic charts and interactive dashboards

Power BI:
1. Data Modeling
  - Importing data from various sources
  - Creating and managing relationships between different datasets
  - Data modeling basics (star schema, snowflake schema)

2. Data Transformation
  - Using Power Query for data cleaning and transformation
  - Advanced data shaping techniques
  - Calculated columns and measures using DAX

3. Data Visualization and Reporting
  - Creating interactive reports and dashboards
  - Visualizations (bar, line, pie charts, maps)
  - Publishing and sharing reports, scheduling data refreshes

Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.

Essential Programming Languages to Learn Data Science 👇👇

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts 👇👇

Data Analysis with R

Intro to Data Science

Practical Python Programming

SQL for Data Analysis

Java Essential Concepts

Machine Learning with Python

Data Science Project Ideas

Learning SQL FREE Book

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Best way to prepare for a SQL interviews 👇👇

1. Review Basic Concepts: Ensure you understand fundamental SQL concepts like SELECT statements, JOINs, GROUP BY, and WHERE clauses.

2. Practice SQL Queries: Work on writing and executing SQL queries. Practice retrieving, updating, and deleting data.

3. Understand Database Design: Learn about normalization, indexes, and relationships to comprehend how databases are structured.

4. Know Your Database: If possible, find out which database system the company uses (e.g., MySQL, PostgreSQL, SQL Server) and familiarize yourself with its specific syntax.

5. Data Types and Constraints: Understand various data types and constraints such as PRIMARY KEY, FOREIGN KEY, and UNIQUE constraints.

6. Stored Procedures and Functions: Learn about stored procedures and functions, as interviewers may inquire about these.

7. Data Manipulation Language (DML): Be familiar with INSERT, UPDATE, and DELETE statements.

8. Data Definition Language (DDL): Understand statements like CREATE, ALTER, and DROP for database and table management.

9. Normalization and Optimization: Brush up on database normalization and optimization techniques to demonstrate your understanding of efficient database design.

10. Troubleshooting Skills: Be prepared to troubleshoot queries, identify errors, and optimize poorly performing queries.

11. Scenario-Based Questions: Practice answering scenario-based questions. Understand how to approach problems and design solutions.

12. Latest Trends: Stay updated on the latest trends in database technologies and SQL best practices.

13. Review Resume Projects: If you have projects involving SQL on your resume, be ready to discuss them in detail.

14. Mock Interviews: Conduct mock interviews with a friend or use online platforms to simulate real interview scenarios.

15. Ask Questions: Prepare questions to ask the interviewer about the company's use of databases and SQL.

Best Resources to learn SQL 👇

SQL Topics for Data Analysts

SQL Udacity Course

Download SQL Cheatsheet

SQL Interview Questions

Learn & Practice SQL

Also try to apply what you learn through hands-on projects or challenges.

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ENJOY LEARNING 👍👍