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Finances Shouldn't matter, Everyone should have right to Learn. > Reminder! Just 4 Days left to claim ultimate Early Bird Discount! Opt for "Easy Islamic Installments" , Grab 2 , 3 & 4 as per your feasibility. Navigate to your favorite Course: 1. Karachi AI | Certified Viz Expert 2. Karachi AI | Certified Data Engineer 3. Karachi AI | Certified Data Analyst Remember there are limited Slots for Early Bird left.

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Decision Delayed is Benefit Expired! > Reminder! Just 10 Days left to claim ultimate Early Bird Discount! Pay a "One Shot Payment" or Opt for "Easy Islamic Installments" Navigate to your favorite course 1. Karachi AI | Certified Viz Expert 2. Karachi AI | Certified Data Engineer 3. Karachi AI | Certified Data Analyst Remember there are limited Slots for Early Bird left.

I recently worked on an Employee Churn Rate prediction model, focusing on data transformation, univariate, bivariate, and multivariate analysis, and feature engineering. 🧠📊 The assignment was overall a great experience. I really enjoyed it and learned a lot! We used three methods: Logistic Regression, Decision Tree, and Random Forest. After selecting the best model, I chose Logistic Regression because the classification report showed it had the best F1 score, recall, and confusion matrix compared to the others. While the model performed well, feedback highlighted the need for scaling and one-hot encoding for some parameters. It was also clarified that the model wasn’t overfitted, as I initially thought. Proud to achieve 68/75 overall in my Data Analyst course by Karachi AI | Certified Data Analyst excelling in areas like de-duplication, univariate analysis, and important feature identification! 🌟 Key takeaway: Feedback refines growth! Always double-check interpretations and keep learning. 💡✨ #MachineLearning #EmployeeChurn #DataAnalyst #HappyLearning

Looking forward to training the students at International Islamic University, Islamabad on 20th November on how SQL is used for data analytics and how they can start their careers in DA with it as a skill and it will be done using Ai DataYard Deployment of SQL which can be accessed without downloading or installing. #Ai #SQL #data #Machinelearning

What is Descriptive Statistics? Descriptive statistics is a branch of statistics that deals with the analysis and description of basic features of data. It provides a summary of the data, including measures of central tendency, variability, and distribution. ☞Types of Descriptive Statistics 1. Measures of Central Tendency: These measures describe the middle or typical value of a dataset. Examples include: - Mean (average) - Median (middle value) - Mode (most frequent value) 2. Measures of Variability: These measures describe the spread or dispersion of a dataset. Examples include: - Range (difference between highest and lowest values) - Variance (average squared difference from the mean) - Standard Deviation (square root of variance) 3. Measures of Distribution: These measures describe the shape and spread of a dataset. Examples include: - Skewness (asymmetry of the distribution) - Kurtosis (tailedness of the distribution) - Histograms and box plots (visual representations of the distribution) ☞Steps in Descriptive Statistics Analysis 1. Data Cleaning: Ensure that the data is accurate, complete, and in the correct format. 2. Data Description: Calculate summary statistics, such as means, medians, and standard deviations. 3. Data Visualization: Use plots and charts to visualize the distribution of the data. 4. Interpretation: Interpret the results in the context of the research question or problem. ☞Common Descriptive Statistics Techniques 1. Frequency Distribution: A table or graph showing the frequency of each value or category. 2. Cross-Tabulation: A table showing the relationship between two or more variables. 3. Correlation Analysis: A technique for measuring the strength and direction of the relationship between two variables. ☞Software for Descriptive Statistics Analysis 1. Microsoft Excel: A popular spreadsheet software for data analysis. 2. IBM SPSS: A statistical software package for data analysis. 3. R: A programming language and software environment for statistical computing and graphics. 4. Python: A programming language and software environment for data analysis and machine learning. 📌 Examples: Frequency Frequency refers to the number of times a particular value or category occurs in a dataset. E.g: Suppose we have a dataset of exam scores for 10 students: Student ID | Score 1 | 80 2 | 70 3 | 80 4 | 90 5 | 70 6 | 80 7 | 90 8 | 70 9 | 80 10 | 90 📌Frequency table: Tables are used to present data in a clear and organized manner. They consist of rows and columns, with each row representing a single observation and each column representing a variable, making it easy to read and understand. Score | Frequency 70 | 3 80 | 5 90 | 2 In this example, the frequency table shows that the score 80 occurs 5 times, the score 70 occurs 3 times, and the score 90 occurs 2 times. See percentage & chart in the comment section Repost ♻️ #teaching #research #statistics

Just wrapped up the Data Analytics workshop at the AI & Data Meetup in Karachi! This event, hosted by NIC Karachi, brought together industry experts who shared valuable insights and answered burning questions about data analytics. From career advice to a hands-on PowerBI workshop led by global trainer Ammar Jamshed (MSc DS) (MSc DS), the event highlighted the importance of data in today's world: 1. Data-driven decisions: Uncover the power of making informed choices based on facts, not just intuition. 2. Enhanced efficiency: Learn how data analysis can optimize processes and save your business time and money. 3. Customer insights: Gain a deeper understanding of your customers and personalize your offerings for better experiences. Ai DataYard #DataAnalytics #AI #PowerBI #NICKarachi #Workshop

🟠🟢🟡 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 is the backbone of modern data and AI. Here are 20 foundational terms every professional should know. 1️⃣ Data Pipeline: Automates data flow from sources to destinations like warehouses 2️⃣ ETL: Extract, clean, and load data for analysis 3️⃣ Data Lake: Stores raw, unstructured data at scale 4️⃣ Data Warehouse: Optimized for structured data and BI 5️⃣ Data Governance: Ensures data accuracy, security, and compliance 6️⃣ Data Quality: Accuracy, consistency, and reliability of data 7️⃣ Data Cleansing: Fixes errors for trustworthy datasets 8️⃣ Data Modeling: Organizes data into structured formats 9️⃣ Data Integration: Combines data from multiple sources 🔟 Data Orchestration: Automates workflows across pipelines 1️⃣1️⃣ Data Transformation: Prepares data for analysis or integration 1️⃣2️⃣ Real-Time Processing: Analyzes data as it’s generated 1️⃣3️⃣ Batch Processing: Processes data in scheduled chunks 1️⃣4️⃣ Cloud Data Platform: Scalable data storage and analytics in the cloud 1️⃣5️⃣ Data Sharding: Splits databases for better performance 1️⃣6️⃣ Data Partitioning: Divides datasets for parallel processing 1️⃣7️⃣ Data Source: Origin of raw data (APIs, files, etc.) 1️⃣8️⃣ Data Schema: Blueprint for database structure 1️⃣9️⃣ DWA: Automates warehouse creation and management 2️⃣0️⃣ Metadata: Context about data (e.g., types, relationships) Which of these terms do you use most often? Let me know in the comments! Thank you Ravit Jain for sharing. 👨👨👦👦 Join my GRC WhatsApp group to get more content — https://lnkd.in/gNgcrzfG #data #ai #dataengineering #cybersecurity

Do you know that data analysts are one of the highest paid professionals in the world? Data analysis involves examining data to extract valuable insights that inform decision-making. By deriving meaning from data, we enhance our ability to make sound choices. Today, we have unprecedented access to vast amounts of data, prompting companies to recognize the advantages of harnessing it. As a result, they increasingly rely on data analysis to uncover insights that drive their business objectives. In today's era of digital transformation, the significance of data analysis has reached unprecedented levels. The surge of data generated by digital technologies has given rise to what we now refer to as "big data." When analyzed effectively, this vast wealth of information can yield crucial insights that have the potential to transform businesses. In the field of data analysis, there is a variety of tools designed to meet diverse needs, complexities, and skill levels. These tools include programming languages such as Python and R, as well as visualization software like Power BI and Tableau, just to mention a few. Ready to take that giant leap towards becoming a data analyst? Join us at Draftek Systems Limited, one of the best IT training centers in Abuja, Nigeria, and together let us take your dreams of becoming a data analyst to greater heights. For the full article, please visit the web page below. https://lnkd.in/dnP-Ce7q

Great SQL interview question shared! A helpful resource for preparing and sharpening your SQL skills. Check it out!