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How to Build CI/CD Pipeline: GitHub to AWS

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 Creating a CI/CD pipeline to deploy a project from GitHub to AWS can be done using various AWS services like AWS CodePipeline, AWS CodeBuild, and optionally AWS CodeDeploy or Amazon ECS for application deployment. Below is a high-level guide on how to set up a basic GitHub to AWS pipeline: Prerequisites AWS Account : Ensure access to the AWS account with the necessary permissions. GitHub Repository : Have your application code hosted on GitHub. IAM Roles : Create necessary IAM roles with permissions to interact with AWS services (e.g., CodePipeline, CodeBuild, S3, ECS, etc.). AWS CLI : Install and configure the AWS CLI for easier management of services. Step 1: Create an S3 Bucket for Artifacts AWS CodePipeline requires an S3 bucket to store artifacts (builds, deployments, etc.). Go to the S3 service in the AWS Management Console. Create a new bucket, ensuring it has a unique name. Note the bucket name for later use. Step 2: Set Up AWS CodeBuild CodeBuild will handle the build proces
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These 10 Skills You Need to Become Data Analyst

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To become a data analyst with Python, there are several technical skills you need to learn. Here are the key ones:


Technical skills to become data analyst



#1 Python Programming


Python is widely used in data analysis due to its simplicity, versatility, and the availability of powerful libraries. You should have a strong understanding of Python fundamentals, including data types, variables, loops, conditional statements, functions, and file handling.

#2 Data Manipulation Libraries


Familiarize yourself with libraries like NumPy and Pandas, which are essential for data manipulation and analysis. NumPy provides support for efficient numerical operations, while Pandas offers data structures (e.g., DataFrames) for easy data manipulation, cleaning, and transformation.


#3 Data Visualization


Gain proficiency in data visualization libraries like Matplotlib and Seaborn. These libraries enable you to create insightful visual representations of data, such as line plots, scatter plots, bar charts, histograms, and heatmaps.


#4 SQL (Structured Query Language)


SQL is crucial for interacting with databases, which are often used to store and retrieve large datasets. Understanding SQL allows you to extract data, perform aggregations, filter records, and join tables. You can use Python libraries like SQLAlchemy to connect to databases and execute SQL queries.


#5 Statistics and Probability


A solid foundation in statistics and probability theory is essential for data analysis. Topics like hypothesis testing, regression analysis, probability distributions, and sampling methods will help you make informed decisions and draw meaningful insights from data.


#6 Exploratory Data Analysis (EDA)


EDA involves examining and summarizing data to identify patterns, anomalies, and relationships. You should learn techniques such as summary statistics, data profiling, data cleaning, and handling missing values. Python libraries like Pandas and NumPy are commonly used in EDA.


#7 Machine Learning


Although not strictly required for data analysis, knowledge of machine learning techniques is highly beneficial. Understanding concepts like supervised and unsupervised learning, feature engineering, model evaluation, and cross-validation can enhance your data analysis capabilities. Python offers popular machine learning libraries such as Scikit-learn and TensorFlow.


#8 Data Wrangling and Cleaning


Real-world data is often messy and requires cleaning and preprocessing before analysis. Learn techniques to handle missing data, outliers, and inconsistencies in datasets. Python libraries like Pandas provide functions for data cleaning and transformation.


#9 Data Analysis Libraries


Explore additional Python libraries like SciPy (scientific computing), StatsModels (statistical modeling), and sci-kit-learn (machine learning). These libraries offer a wide range of tools and functions that can support advanced data analysis tasks.

#10 Communication and Documentation



While not strictly technical skills, effective communication, and documentation are crucial for data analysts. You should be able to present your findings and insights clearly to both technical and non-technical audiences. Learn how to create visually appealing charts and graphs, write clear reports, and effectively communicate your analysis.



The bottom line

Remember that becoming a proficient data analyst is a continuous learning process. Stay updated with the latest trends, explore new libraries, participate in online communities, and work on real-world projects to further enhance your skills.

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