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Data Analytics

Data Analytics

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We're here to empower you to turn your data ambitions into reality! šŸ˜ƒ Whether you're just starting out in the world of data analytics or aiming to elevate your career to new heights, our team is dedicated to offering you expert guidance to become a data analyst. Follow this page to learn SQL, Python, Alteryx, Tableau,Power BI and many more For collaborations/ promotions, contact dataanalyticslink@gmail.com

Website
https://telegram.me/sqlspecialist
Industry
E-Learning Providers
Company size
2-10 employees
Headquarters
New York
Type
Self-Employed
Founded
2023

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  • Data Analytics reposted this

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    Data Analytics

    226,239 followers

    Anyone with an Internet connection can learn š——š—®š˜š—® š—”š—»š—®š—¹š˜†š˜€š—¶š˜€ š—³š—¼š—æ š—³š—暝—²š—²: No more excuses now. SQL - https://lnkd.in/gQkjdAWP Python - https://lnkd.in/gQk8siKn Excel - https://lnkd.in/d-txjPJn Power BI - https://lnkd.in/gs6RgH2m Tableau - https://lnkd.in/dDFdyS8y Data Visualization - https://lnkd.in/dcHqhgn4 Data Cleaning - https://lnkd.in/dCXspR4p Google Sheets - https://lnkd.in/d7eDi8pn Statistics - https://lnkd.in/dgaw6KMW Projects - https://lnkd.in/g2Fjzbma Portfolio - https://t.me/DataPortfolio If you've read so far, do LIKE and share this channel with your friends & loved ones ā™„ļø Hope it helps :)

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  • View organization page for Data Analytics
    Data Analytics

    226,239 followers

    Anyone with an Internet connection can learn š——š—®š˜š—® š—”š—»š—®š—¹š˜†š˜€š—¶š˜€ š—³š—¼š—æ š—³š—暝—²š—²: No more excuses now. SQL - https://lnkd.in/gQkjdAWP Python - https://lnkd.in/gQk8siKn Excel - https://lnkd.in/d-txjPJn Power BI - https://lnkd.in/gs6RgH2m Tableau - https://lnkd.in/dDFdyS8y Data Visualization - https://lnkd.in/dcHqhgn4 Data Cleaning - https://lnkd.in/dCXspR4p Google Sheets - https://lnkd.in/d7eDi8pn Statistics - https://lnkd.in/dgaw6KMW Projects - https://lnkd.in/g2Fjzbma Portfolio - https://t.me/DataPortfolio If you've read so far, do LIKE and share this channel with your friends & loved ones ā™„ļø Hope it helps :)

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      +1

  • Data Analytics reposted this

    View organization page for Data Analytics
    Data Analytics

    226,239 followers

    Python for Data Analysts: From Basics to Advanced Level šŸ”¹ Basics of Python āžŠ Python Syntax & Data Types ā†³ Variables, data types (int, float, string, bool) ā†³ Type conversion and basic operations āž‹ Control Flow & Loops ā†³ if-else, for, while loops ā†³ List comprehensions for efficient iteration āžŒ Functions & Lambda Expressions ā†³ Defining functions and using *args & **kwargs ā†³ Anonymous functions with lambda āž Error Handling ā†³ try-except for handling errors gracefully ā†³ Raising custom exceptions šŸ”¹ Intermediate Python for Data Analytics āžŽ Working with Lists, Tuples, and Dictionaries ā†³ List, tuple, and dictionary operations ā†³ Dictionary and list comprehensions āž String Manipulation & Regular Expressions ā†³ String formatting and manipulation ā†³ Extracting patterns with re module āž Date & Time Handling ā†³ Working with datetime and pandas.to_datetime() ā†³ Formatting, extracting, and calculating time differences āž‘ File Handling (CSV, JSON, Excel) ā†³ Reading and writing structured files using pandas ā†³ Handling large files efficiently using chunks šŸ”¹ Data Analysis with Python āž’ Pandas for Data Manipulation ā†³ Reading, cleaning, filtering, and transforming data ā†³ Aggregations using .groupby(), .pivot_table() ā†³ Merging and joining datasets āž“ NumPy for Numerical Computing ā†³ Creating and manipulating arrays ā†³ Vectorized operations for performance optimization ā“« Handling Missing Data ā†³ .fillna(), .dropna(), .interpolate() ā†³ Imputing missing values for better analytics ā“¬ Data Visualization with Matplotlib & Seaborn ā†³ Creating plots (line, bar, scatter, histogram) ā†³ Customizing plots for presentations ā†³ Heatmaps for correlation analysis šŸ”¹ Advanced Topics for Data Analysts ā“­ SQL with Python ā†³ Connecting to databases using sqlalchemy ā†³ Writing and executing SQL queries in Python (pandas.read_sql()) ā†³ Merging SQL and Pandas for analysis ā“® Working with APIs & Web Scraping ā†³ Fetching data from APIs using requests ā†³ Web scraping using BeautifulSoup and Selenium ā“Æ ETL (Extract, Transform, Load) Pipelines ā†³ Automating data ingestion and transformation ā†³ Cleaning and loading data into databases ā“° Time Series Analysis ā†³ Working with time-series data in Pandas ā†³ Forecasting trends using moving averages ā“± Machine Learning Basics for Data Analysts ā†³ Introduction to Scikit-learn (Linear Regression, KNN, Clustering) ā†³ Feature engineering and model evaluation šŸš€ The best way to learn Python is by working on real-world projects! Data Analytics Projects: https://t.me/sqlproject Share with credits: https://t.me/sqlspecialist Hope it helps :)

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      +4

  • Data Analytics reposted this

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    Data Analytics

    226,239 followers

    Everything you need to learn Python for FREE āœ… Python Resources: https://lnkd.in/gQk8siKn Python Projects: https://lnkd.in/dbbReX7H Web Development: https://lnkd.in/gj3dmvgQ Data Analysts: https://lnkd.in/ds3J-w4b Data Science: https://lnkd.in/g2Fjzbma Machine Learning: https://lnkd.in/ddhUzMGC Python for Data Science: https://lnkd.in/dNSst9s7 Artificial Intelligence: https://lnkd.in/dyEZQwXv FREE Courses: https://lnkd.in/gMGmeB-2 Like for more ā™„ļø

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  • Data Analytics reposted this

    View organization page for Data Analytics
    Data Analytics

    226,239 followers

    Complete step-by-step syllabus of #Excel for Data Analytics Introduction to Excel for Data Analytics: Overview of Excel's capabilities for data analysis Introduction to Excel's interface: ribbons, worksheets, cells, etc. Differences between Excel desktop version and Excel Online (web version) Data Import and Preparation: Importing data from various sources: CSV, text files, databases, web queries, etc. Data cleaning and manipulation techniques: sorting, filtering, removing duplicates, etc. Data types and formatting in Excel Data validation and error handling Data Analysis Techniques in Excel: Basic formulas and functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, etc. Advanced functions for data analysis: INDEX-MATCH, SUMIFS, COUNTIFS, etc. PivotTables and PivotCharts for summarizing and analyzing data Advanced data analysis tools: Goal Seek, Solver, What-If Analysis, etc. Data Visualization in Excel: Creating basic charts: column, bar, line, pie, scatter, etc. Formatting and customizing charts for better visualization Using sparklines for visualizing trends in data Creating interactive dashboards with slicers and timelines Advanced Data Analysis Features: Data modeling with Excel Tables and Relationships Using Power Query for data transformation and cleaning Introduction to Power Pivot for data modeling and DAX calculations Advanced charting techniques: combination charts, waterfall charts, etc. Statistical Analysis in Excel: Descriptive statistics: mean, median, mode, standard deviation, etc. Hypothesis testing: t-tests, chi-square tests, ANOVA, etc. Regression analysis and correlation Forecasting techniques: moving averages, exponential smoothing, etc. Data Visualization Tools in Excel: Introduction to Excel add-ins for enhanced visualization (e.g., Power Map, Power View) Creating interactive reports with Excel add-ins Introduction to Excel Data Model for handling large datasets Real-world Projects and Case Studies: Analyzing real-world datasets Solving business problems with Excel Portfolio development showcasing Excel skills Free Excel Resources: https://lnkd.in/d-txjPJn Share our channel link with your true friends: https://t.me/excel_analyst Hope this helps you šŸ˜Š

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  • View organization page for Data Analytics
    Data Analytics

    226,239 followers

    Python for Data Analysts: From Basics to Advanced Level šŸ”¹ Basics of Python āžŠ Python Syntax & Data Types ā†³ Variables, data types (int, float, string, bool) ā†³ Type conversion and basic operations āž‹ Control Flow & Loops ā†³ if-else, for, while loops ā†³ List comprehensions for efficient iteration āžŒ Functions & Lambda Expressions ā†³ Defining functions and using *args & **kwargs ā†³ Anonymous functions with lambda āž Error Handling ā†³ try-except for handling errors gracefully ā†³ Raising custom exceptions šŸ”¹ Intermediate Python for Data Analytics āžŽ Working with Lists, Tuples, and Dictionaries ā†³ List, tuple, and dictionary operations ā†³ Dictionary and list comprehensions āž String Manipulation & Regular Expressions ā†³ String formatting and manipulation ā†³ Extracting patterns with re module āž Date & Time Handling ā†³ Working with datetime and pandas.to_datetime() ā†³ Formatting, extracting, and calculating time differences āž‘ File Handling (CSV, JSON, Excel) ā†³ Reading and writing structured files using pandas ā†³ Handling large files efficiently using chunks šŸ”¹ Data Analysis with Python āž’ Pandas for Data Manipulation ā†³ Reading, cleaning, filtering, and transforming data ā†³ Aggregations using .groupby(), .pivot_table() ā†³ Merging and joining datasets āž“ NumPy for Numerical Computing ā†³ Creating and manipulating arrays ā†³ Vectorized operations for performance optimization ā“« Handling Missing Data ā†³ .fillna(), .dropna(), .interpolate() ā†³ Imputing missing values for better analytics ā“¬ Data Visualization with Matplotlib & Seaborn ā†³ Creating plots (line, bar, scatter, histogram) ā†³ Customizing plots for presentations ā†³ Heatmaps for correlation analysis šŸ”¹ Advanced Topics for Data Analysts ā“­ SQL with Python ā†³ Connecting to databases using sqlalchemy ā†³ Writing and executing SQL queries in Python (pandas.read_sql()) ā†³ Merging SQL and Pandas for analysis ā“® Working with APIs & Web Scraping ā†³ Fetching data from APIs using requests ā†³ Web scraping using BeautifulSoup and Selenium ā“Æ ETL (Extract, Transform, Load) Pipelines ā†³ Automating data ingestion and transformation ā†³ Cleaning and loading data into databases ā“° Time Series Analysis ā†³ Working with time-series data in Pandas ā†³ Forecasting trends using moving averages ā“± Machine Learning Basics for Data Analysts ā†³ Introduction to Scikit-learn (Linear Regression, KNN, Clustering) ā†³ Feature engineering and model evaluation šŸš€ The best way to learn Python is by working on real-world projects! Data Analytics Projects: https://t.me/sqlproject Share with credits: https://t.me/sqlspecialist Hope it helps :)

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      +4
  • View organization page for Data Analytics
    Data Analytics

    226,239 followers

    Complete Data Science Roadmap  šŸ‘‡šŸ‘‡  1. Introduction to Data Science     - Overview and Importance     - Data Science Lifecycle     - Key Roles (Data Scientist, Analyst, Engineer)  2. Mathematics and Statistics     - Probability and Distributions     - Descriptive/Inferential Statistics     - Hypothesis Testing     - Linear Algebra and Calculus Basics  3. Programming Languages     - Python: NumPy, Pandas, Matplotlib     - R: dplyr, ggplot2     - SQL: Joins, Aggregations, CRUD  4. Data Collection & Preprocessing     - Data Cleaning and Wrangling     - Handling Missing Data     - Feature Engineering  5. Exploratory Data Analysis (EDA)     - Summary Statistics     - Data Visualization (Histograms, Box Plots, Correlation)  6. Machine Learning     - Supervised (Linear/Logistic Regression, Decision Trees)     - Unsupervised (K-Means, PCA)     - Model Selection and Cross-Validation  7. Advanced Machine Learning     - SVM, Random Forests, Boosting     - Neural Networks Basics  8. Deep Learning     - Neural Networks Architecture     - CNNs for Image Data     - RNNs for Sequential Data  9. Natural Language Processing (NLP)     - Text Preprocessing     - Sentiment Analysis     - Word Embeddings (Word2Vec)  10. Data Visualization & Storytelling     - Dashboards (Tableau, Power BI)     - Telling Stories with Data  11. Model Deployment     - Deploy with Flask or Django     - Monitoring and Retraining Models  12. Big Data & Cloud     - Introduction to Hadoop, Spark     - Cloud Tools (AWS, Google Cloud)  13. Data Engineering Basics     - ETL Pipelines     - Data Warehousing (Redshift, BigQuery)  14. Ethics in Data Science     - Ethical Data Usage     - Bias in AI Models  15. Tools for Data Science     - Jupyter, Git, Docker  16. Career Path & Certifications     - Building a Data Science Portfolio  Like if you need similar content šŸ˜„šŸ‘ Free Data Science Resources šŸ‘‡šŸ‘‡ https://lnkd.in/gKfXmy3E

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