Python Learning Series Part-14

Complete Python Topics for Data Analysis: https://t.iss.one/sqlspecialist/548

14. Transfer Learning with Pre-trained Models:

Transfer learning involves using pre-trained models as a starting point for a new task. It's a powerful technique that leverages the knowledge gained from training on large datasets.

1. Introduction to Transfer Learning:
- Why Transfer Learning?
- Utilize knowledge learned from one task to improve performance on a different, but related, task.

- Pre-trained Models:
- Models trained on massive datasets, such as ImageNet, that capture general features of images, text, or other data.

2. Transfer Learning in Computer Vision:
- Fine-tuning Pre-trained Models:
- Adjust the weights of a pre-trained model on a smaller dataset for a specific task.

       base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
       base_model.trainable = False  # Freeze the pre-trained layers

       model = tf.keras.Sequential([
           base_model,
           tf.keras.layers.GlobalAveragePooling2D(),
           tf.keras.layers.Dense(10, activation='softmax')
       ])
       

- Feature Extraction:
- Use pre-trained models as feature extractors.

       base_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

       for layer in base_model.layers:
           layer.trainable = False  # Freeze pre-trained layers

       model = tf.keras.Sequential([
           base_model,
           tf.keras.layers.Flatten(),
           tf.keras.layers.Dense(10, activation='softmax')
       ])
       

3. Transfer Learning in Natural Language Processing:
- Using Pre-trained Embeddings:
- Utilize word embeddings trained on large text corpora.

       embeddings_index = load_pretrained_word_embeddings()
       embedding_matrix = create_embedding_matrix(word_index, embeddings_index)
       embedding_layer = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, weights=[embedding_matrix], input_length=max_length)
       

- Fine-tuning Language Models:
- Fine-tune models like BERT for specific tasks.

       bert_model = TFBertModel.from_pretrained('bert-base-uncased')
       

Transfer learning accelerates model development by leveraging pre-existing knowledge.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Python Learning Series Part-15

Complete Python Topics for Data Analysis: https://t.iss.one/sqlspecialist/548

15. Big Data Processing with Apache Spark:

Apache Spark is a powerful open-source distributed computing system that provides fast and general-purpose cluster computing for big data processing. It is designed to be fast and flexible, supporting various programming languages, including Python.

1. Introduction to Apache Spark:
- Cluster Computing:
- Distributes data processing tasks across a cluster of machines.

- Resilient Distributed Datasets (RDDs):
- Basic unit of data in Spark, partitioned across nodes in the cluster.

       from pyspark import SparkContext

       sc = SparkContext("local", "First App")
       data = [1, 2, 3, 4, 5]
       rdd = sc.parallelize(data)
       

2. Spark Transformations and Actions:
- Transformations:
- Operations that create a new RDD from an existing one (e.g., map, filter).

       squared_rdd = rdd.map(lambda x: x**2)
       

- Actions:
- Operations that return a value to the driver program or write data to an external storage system (e.g., reduce, collect).

       total_sum = squared_rdd.reduce(lambda x, y: x + y)
       

3. PySpark:
- Python API for Spark:
- PySpark allows you to use Spark capabilities within Python.

       from pyspark.sql import SparkSession

       spark = SparkSession.builder.appName("example").getOrCreate()
       

- DataFrames in PySpark:
- A distributed collection of data organized into named columns.

       # Create a DataFrame from a CSV file
       df = spark.read.csv("file.csv", header=True, inferSchema=True)
       

4. Spark SQL:
- Structured Query Language:
- Allows querying structured data using SQL queries.

       df.createOrReplaceTempView("my_table")
       result = spark.sql("SELECT * FROM my_table WHERE age > 21")
       

5. Spark Machine Learning (MLlib):
- Machine Learning Library:
- Provides scalable machine learning algorithms.

       from pyspark.ml.regression import LinearRegression

       # Example linear regression
       lr = LinearRegression(featuresCol="features", labelCol="label")
       model = lr.fit(training_data)
       

- Integration with Scikit-Learn:
- Use Spark for distributed training with scikit-learn API.

       from pyspark.ml import Estimator

       class SparkMLlibEstimator(Estimator):
           def fit(self, dataset):
               # Distributed training logic
               return trained_model
       

It's essential to note that this topic is a bit advanced and may be considered optional for data analysts. While understanding Spark can be highly beneficial for handling large-scale data processing, analysts may choose to explore it based on the specific requirements and complexity of their data tasks.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

SQL & Python Learning Series completed. Should we go with Power BI next?
Like if you want to learn Power BI 👍

Complete Power BI Topics for Data Analysts 👇👇

1. Introduction to Power BI
- Overview and architecture
- Installation and setup

2. Loading and Transforming Data
- Connecting to various data sources
- Data loading techniques
- Data cleaning and transformation using Power Query

3. Data Modeling
- Creating relationships between tables
- DAX (Data Analysis Expressions) basics
- Calculated columns and measures

4. Data Visualization
- Building reports and dashboards
- Visualization best practices
- Custom visuals and formatting options

5. Advanced DAX
- Time intelligence functions
- Advanced DAX functions and scenarios
- Row context vs. filter context

6. Power BI Service
- Publishing and sharing reports
- Power BI workspaces and apps
- Power BI mobile app

7. Power BI Integration
- Integrating Power BI with other Microsoft tools (Excel, SharePoint, Teams)
- Embedding Power BI reports in websites and applications

8. Power BI Security
- Row-level security
- Data source permissions
- Power BI service security features

9. Power BI Governance
- Monitoring and managing usage
- Best practices for deployment
- Version control and deployment pipelines

10. Advanced Visualizations
- Drillthrough and bookmarks
- Hierarchies and custom visuals
- Geo-spatial visualizations

11. Power BI Tips and Tricks
- Productivity shortcuts
- Data exploration techniques
- Troubleshooting common issues

12. Power BI and AI Integration
- AI-powered features in Power BI
- Azure Machine Learning integration
- Advanced analytics in Power BI

13. Power BI Report Server
- On-premises deployment
- Managing and securing on-premises reports
- Power BI Report Server vs. Power BI Service

14. Real-world Use Cases
- Case studies and examples
- Industry-specific applications
- Practical scenarios and solutions

You can refer this Power BI Resources to learn more

Like this post if you want me to continue this Power BI series 👍♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Thanks for the amazing response guys on above post 😄

Complete Power BI Topics for Data Analysis

-> https://t.iss.one/sqlspecialist/588

Let's start with Part-1 today

1. Introduction to Power BI:
- Overview and architecture: Power BI is a business analytics tool by Microsoft, enabling users to visualize and share insights from their data. It includes components like Power BI Desktop for creating reports, Power BI Service for sharing and collaborating, and Power BI Mobile for on-the-go access.

- Installation and setup: To get started, you need to download and install Power BI Desktop. After that, you can connect to various data sources and begin building your reports and dashboards.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-2

Complete Power BI Topics for Data Analysis 👇
https://t.iss.one/sqlspecialist/588

Loading and Transforming Data:

- Connecting to various data sources: Power BI allows you to connect to a wide range of data sources including Excel files, databases (SQL Server, MySQL, Oracle), cloud services (Azure, Salesforce), web sources, and more.

- Data loading techniques: Once connected, you can import data into Power BI or use DirectQuery to query data live from the source. Importing data caches it within the Power BI file, while DirectQuery accesses it directly from the source.

- Data cleaning and transformation using Power Query: Power Query is a powerful tool within Power BI for data cleaning and transformation. It allows you to perform tasks like removing duplicates, splitting columns, merging tables, and more to prepare your data for analysis.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-3

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Data Modeling:
- Creating relationships between tables: In Power BI, you can model your data by creating relationships between different tables. This enables you to combine data from multiple sources and analyze it together.

- DAX (Data Analysis Expressions) basics: DAX is a formula language used in Power BI to create calculated columns, measures, and calculated tables. It allows for advanced calculations and manipulation of data within your reports.

- Calculated columns and measures: Calculated columns are columns in your dataset that are calculated based on a formula, while measures are calculations that are dynamically evaluated based on the context of the data being displayed. Both are essential for performing complex analyses in Power BI.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-4

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Data Visualization:
- Building reports and dashboards: Power BI enables users to create interactive reports and dashboards by dragging and dropping visual elements onto the canvas. Reports can include various visuals such as charts, graphs, tables, maps, and more.

- Visualization best practices: Effective visualization is crucial for conveying insights from data. Power BI provides a wide range of customization options for formatting visuals, choosing appropriate chart types, and arranging elements to optimize readability and understanding.

- Custom visuals and formatting options: Power BI allows users to extend its visualization capabilities by importing custom visuals from the marketplace or building their own using developer tools. Additionally, there are numerous formatting options available to customize the appearance of visuals according to your preferences.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-5

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Today, we will learn about advanced DAX:

- Time intelligence functions: These are DAX functions specifically designed to analyze data over time periods. For instance, TOTALYTD calculates the year-to-date total for a measure. It's useful for comparing cumulative values such as sales or expenses across different time frames.

- Advanced DAX functions and scenarios: DAX offers various advanced functions for complex calculations. For example, RANKX ranks values dynamically based on specified criteria, enabling you to determine the ranking of products by sales volume or customers by satisfaction score.

Real-world scenerios:

- Time intelligence functions: Let's say you want to analyze monthly sales trends and compare them year-over-year. You can use TOTALYTD to calculate the total sales up to the current month for each year. This allows you to see if sales are increasing or decreasing compared to the same period in previous years.

- Advanced DAX functions and scenarios: Suppose you're analyzing customer churn rates and want to identify high-value customers at risk of leaving. Using RANKX, you can rank customers based on their lifetime value or purchase frequency. This helps prioritize retention efforts on customers most valuable to the business.

- Row context vs. filter context: DAX calculations in Power BI are evaluated within either row context or filter context, depending on the context in which they are used. Understanding the difference between these contexts is crucial for writing accurate and efficient DAX formulas.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-6

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Now, let's discuss about Power BI Service in detail:

- Publishing and sharing reports: Once you've created reports in Power BI Desktop, you can publish them to the Power BI Service. Publishing enables easy sharing and collaboration, allowing colleagues to access and interact with the reports online. Users can view reports, apply filters, and even create their own visualizations.

- Power BI workspaces and apps: Power BI workspaces are collaborative environments where users can share and collaborate on content. Within a workspace, you can create and organize reports, dashboards, and datasets. Apps allow you to package this content and distribute it to specific groups of users within your organization, making it easy for teams to access relevant insights.

Real-world Scenerio:

- Publishing and sharing reports: Imagine you've created a sales performance dashboard in Power BI Desktop, showcasing key metrics such as revenue, units sold, and top-performing products. By publishing this dashboard to the Power BI Service, your sales team can access it from anywhere with an internet connection. They can monitor sales performance in real-time, drill down into specific regions or product categories, and collaborate on strategies to improve sales.

- Power BI workspaces and apps: Within a marketing analytics workspace, you can collaborate with your marketing team on various reports and dashboards related to campaign performance, website analytics, and customer segmentation. By packaging these resources into an app tailored for the marketing department, you streamline access to critical insights and ensure everyone is working with the same up-to-date information.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

On your request, I am continuing Power BI Learning Series. Planning to also start Tableau & Excel Learning Series 😄

Power BI LEARNING SERIES PART-7

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Today, let's discuss about Power BI Integration in detail:

- Integrating Power BI with other Microsoft tools: Power BI seamlessly integrates with other Microsoft tools such as Excel, SharePoint, and Teams. This integration allows users to embed Power BI reports and dashboards directly into these applications, enhancing data accessibility and collaboration.

- Embedding Power BI reports in websites and applications: Power BI provides capabilities for embedding reports and dashboards into custom websites and applications. This allows organizations to share insights with external stakeholders or embed analytics directly into customer-facing applications.

Example:

- Integrating Power BI with Excel: You can connect Power BI to Excel data models, enabling Excel users to leverage Power BI's visualization capabilities without leaving the familiar Excel interface. This integration streamlines the process of creating dynamic reports and dashboards using Excel data.

- Embedding Power BI reports in SharePoint: By embedding Power BI reports into SharePoint pages, you can create interactive data portals for teams or departments. For example, a sales team's SharePoint site can feature embedded Power BI dashboards showcasing sales performance metrics, pipeline analysis, and forecasts.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-8

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Today, let's discuss about Power BI Security: 👇

- Row-level security: Power BI allows you to implement row-level security to restrict data access based on user roles or criteria. This ensures that users only see the data relevant to their role or permissions, maintaining data confidentiality and integrity.

- Data source permissions: Power BI integrates with various data sources, and it's essential to manage permissions at both the dataset and data source levels. This ensures that only authorized users can access and interact with the underlying data.

- Power BI service security features: The Power BI service offers additional security features such as encryption, multi-factor authentication, and activity logging. These features help protect data both at rest and in transit, safeguarding it against unauthorized access and ensuring compliance with security regulations.

Example:

- Row-level security: In a sales organization, you can implement row-level security to restrict sales representatives' access to only the accounts or territories they are responsible for. This ensures that each salesperson can only view and analyze data relevant to their assigned accounts, protecting sensitive information and preventing data leakage.

- Data source permissions: Suppose your organization stores sensitive HR data in an on-premises SQL Server database. When connecting Power BI to this database, you can configure data source permissions to grant access only to HR managers and administrators, ensuring that only authorized personnel can access and analyze HR-related insights.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-9

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Let's learn about Power BI Governance in detail today:

- Monitoring and managing usage: Power BI governance involves monitoring and managing the usage of Power BI resources within an organization. This includes tracking user activity, analyzing usage patterns, and optimizing resource allocation to ensure efficient utilization of Power BI resources.

- Best practices for deployment: Implementing best practices for deploying Power BI resources helps maintain consistency, reliability, and scalability across the organization. This includes standardized naming conventions, folder structures, and deployment processes to streamline development and deployment workflows.

- Version control and deployment pipelines: Version control and deployment pipelines ensure that changes to Power BI reports, dashboards, and datasets are properly managed and deployed in a controlled manner. This helps prevent issues such as conflicting changes, data inconsistencies, and deployment errors.

Example:

- Monitoring and managing usage: A Power BI administrator can use usage metrics and audit logs to track user activity, identify underutilized resources, and optimize license allocation. By analyzing usage patterns, the administrator can ensure that resources are allocated efficiently and users have access to the insights they need.

- Best practices for deployment: Implementing a standardized deployment process, such as using development, testing, and production environments, ensures that changes to Power BI content are thoroughly tested before being deployed to production. This reduces the risk of errors and disruptions to business operations.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-10

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Now, let's talk about Advanced Visualizations:

- Drillthrough and bookmarks: Drillthrough allows users to navigate from a summary report to a detailed report by clicking on a data point. Bookmarks enable users to save and revisit specific states of a report, facilitating storytelling and interactive exploration of data.

- Hierarchies and custom visuals: Hierarchies allow users to drill down into data at different levels of granularity, such as year, quarter, month, and day. Custom visuals extend Power BI's visualization capabilities by enabling users to create unique and specialized visualizations tailored to their specific needs.

- Geo-spatial visualizations: Power BI supports geo-spatial visualizations such as maps, which allow users to visualize data based on geographic locations. This is useful for analyzing regional trends, identifying geographic patterns, and gaining insights from location-based data.

Example:

- Drillthrough and bookmarks: In a sales performance dashboard, users can drill through from a high-level summary of sales revenue to a detailed report showing sales by product category, customer segment, or geographical region. Bookmarks can be used to save specific views of the data, such as a filtered view for a particular sales territory or time period.

- Hierarchies and custom visuals: A financial analysis dashboard may include a hierarchical view of financial data, allowing users to drill down from an overview of total revenue to detailed breakdowns by product, customer, and region. Custom visuals, such as a waterfall chart or a Sankey diagram, can provide additional insights into financial performance and trends.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-11

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Now, let's discuss some Power BI Tips and Tricks:

- Productivity shortcuts: Power BI offers various productivity shortcuts and time-saving techniques to streamline report development and analysis. These include keyboard shortcuts, drag-and-drop functionality, and quick access to commonly used features.

- Data exploration techniques: Power BI provides tools and features for interactive data exploration, allowing users to quickly uncover insights and patterns within their data. Techniques such as slicing and dicing, filtering, and drill-down enable users to analyze data from different perspectives and levels of detail.

- Troubleshooting common issues: Power BI users may encounter common issues such as data refresh failures, visual formatting inconsistencies, or performance bottlenecks. Knowing how to troubleshoot these issues, using techniques such as reviewing error messages, checking data source connections, and optimizing report performance, can help ensure a smooth user experience.

Example:

- Productivity shortcuts: Instead of manually formatting each visual in a report, users can use the "Format Painter" tool to quickly apply formatting from one visual to another. This saves time and ensures consistency across visuals.

- Data exploration techniques: When analyzing sales data, users can use the "Drill Down" feature to explore sales performance at different levels of detail, such as by year, quarter, month, or day. This helps identify trends and anomalies in the data and provides actionable insights for decision-making.

- Troubleshooting common issues: If a report is not displaying the expected results after a data refresh, users can check the data source connection settings and verify that the data refresh schedule is configured correctly. They can also review error messages in the Power BI service to identify any issues with data transformation or query execution.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-12

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Today's, let's discuss about Power BI and AI Integration:

- AI-powered features in Power BI: Power BI integrates AI capabilities to enhance data analysis and visualization. This includes features such as Q&A (natural language querying), automatic insights, and AI visuals (e.g., decomposition tree, key influencers) that provide automated analysis and recommendations based on the data.

- Azure Machine Learning integration: Power BI enables integration with Azure Machine Learning, allowing users to leverage machine learning models directly within their Power BI reports and dashboards. This integration enables advanced analytics such as predictive forecasting, anomaly detection, and sentiment analysis.

- Advanced analytics in Power BI: Power BI supports advanced analytics scenarios such as clustering, regression analysis, and time series forecasting through built-in features and integration with external tools like R and Python. This allows users to perform sophisticated analyses and gain deeper insights from their data.

Example:

- AI-powered features in Power BI: A marketing team can use Power BI's automatic insights feature to quickly identify trends and patterns in customer behavior. For example, Power BI might automatically detect a spike in website traffic following a social media campaign and suggest further analysis to determine the campaign's effectiveness.

- Azure Machine Learning integration: A retail company can use Azure Machine Learning to build a predictive model for forecasting sales. This model can be deployed to Azure Machine Learning Service and integrated with Power BI to generate real-time forecasts and insights into sales trends, enabling better inventory management and decision-making.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-13

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Now, let's discuss about Power BI Report Server in detail:

- On-premises deployment: Power BI Report Server allows organizations to host and manage Power BI reports and dashboards on their own on-premises servers. This provides greater control over data security and compliance, especially for organizations with strict regulatory requirements or data privacy concerns.

- Managing and securing on-premises reports: Power BI Report Server enables administrators to manage user access, permissions, and content within the on-premises environment. This includes configuring role-based security, auditing user activity, and controlling data source connections to ensure data governance and compliance.

- Power BI Report Server vs. Power BI Service: While Power BI Report Server offers similar capabilities to the Power BI Service, there are some key differences. Power BI Report Server is designed for on-premises deployment and caters to organizations that prefer to host their BI infrastructure internally. In contrast, the Power BI Service is a cloud-based platform managed by Microsoft, offering additional features such as automatic updates, scalable storage, and built-in collaboration tools.

Example:

- On-premises deployment: A financial institution with strict data security requirements opts to deploy Power BI Report Server on its own servers to host financial reports and dashboards. This allows the organization to maintain full control over its BI environment and ensure compliance with regulatory standards such as GDPR or HIPAA.

- Managing and securing on-premises reports: The IT department of a healthcare organization configures role-based security in Power BI Report Server to restrict access to patient data based on user roles and responsibilities. Only authorized healthcare professionals can access sensitive patient information, while administrative staff have access to broader operational reports.

While it's a bit advanced topic, knowing about Power BI Report Server can be very beneficial.

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Power BI LEARNING SERIES PART-14

Complete Power BI Topics for Data Analysis 👇
-> https://t.iss.one/sqlspecialist/588

Now, let's discuss some Real-world Power BI Use Cases 👇

- Case studies and examples: Real-world use cases demonstrate how organizations across various industries leverage Power BI to gain insights, drive decision-making, and improve business outcomes. These case studies showcase the versatility and effectiveness of Power BI in solving diverse business challenges, from sales forecasting to customer segmentation to supply chain optimization.

- Industry-specific applications: Power BI is used across industries such as retail, healthcare, finance, manufacturing, and more. Each industry has unique data analysis requirements and business objectives, and Power BI can be tailored to address specific industry challenges and opportunities. Industry-specific applications highlight how Power BI is customized and applied to meet the needs of different sectors.

- Practical scenarios and solutions: Practical scenarios illustrate common business problems and the solutions that Power BI offers. Whether it's analyzing sales performance, monitoring operational efficiency, or tracking customer satisfaction, Power BI provides tools and capabilities to extract insights from data and drive informed decision-making.

Example:

- Retail: A retail chain uses Power BI to analyze sales data across its stores, identify trends in product sales, and optimize inventory management. By visualizing sales performance by product category, region, and time period, the company can make data-driven decisions to adjust pricing, promotions, and product assortments to maximize profitability.

- Healthcare: A healthcare provider leverages Power BI to analyze patient demographics, treatment outcomes, and resource utilization. By visualizing patient wait times, appointment scheduling efficiency, and patient satisfaction scores, the organization can identify bottlenecks in patient care processes and implement improvements to enhance the overall patient experience.

I hope you guys learnt a lot about Power BI through this learning series.

You can refer this Power BI Resources to learn more.

Like this post you want me to start Tableau or Excel Learning Series 😄

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch.

Here are the links to the Python series

Complete Python Topics for Data Analyst: https://t.iss.one/sqlspecialist/548

Part-1: https://t.iss.one/sqlspecialist/562

Part-2: https://t.iss.one/sqlspecialist/564

Part-3: https://t.iss.one/sqlspecialist/565

Part-4: https://t.iss.one/sqlspecialist/566

Part-5: https://t.iss.one/sqlspecialist/568

Part-6: https://t.iss.one/sqlspecialist/570

Part-7: https://t.iss.one/sqlspecialist/571

Part-8: https://t.iss.one/sqlspecialist/572

Part-9: https://t.iss.one/sqlspecialist/578

Part-10: https://t.iss.one/sqlspecialist/577

Part-11: https://t.iss.one/sqlspecialist/578

Part-12:
https://t.iss.one/sqlspecialist/581

Part-13: https://t.iss.one/sqlspecialist/583

Part-14: https://t.iss.one/sqlspecialist/584

Part-15: https://t.iss.one/sqlspecialist/585

I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.

But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.

Complete SQL Topics for Data Analysts: https://t.iss.one/sqlspecialist/523

Complete Power BI Topics for Data Analysts: https://t.iss.one/sqlspecialist/588

I'll continue with learning series on Excel & Tableau.

Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.

Hope it helps :)