Showing posts with the label Project

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8 Ways to Optimize AWS Glue Jobs in a Nutshell

  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif

A Beginner's Guide to Pandas Project for Immediate Practice

Pandas is a powerful data manipulation and analysis library in Python that provides a wide range of functions and tools to work with structured data. Whether you are a data scientist, analyst, or just a curious learner, Pandas can help you efficiently handle and analyze data.  In this blog post, we will walk through a step-by-step guide on how to start a Pandas project from scratch. By following these steps, you will be able to import data, explore and manipulate it, perform calculations and transformations, and save the results for further analysis. So let's dive into the world of Pandas and get started with your own project! Simple Pandas project Import the necessary libraries: import pandas as pd import numpy as np Read data from a file into a Pandas DataFrame: df = pd.read_csv('/path/to/file.csv') Explore and manipulate the data: View the first few rows of the DataFrame: print(df.head()) Access specific columns or rows in the DataFrame: print(df['column_name'])

AI Project 5 things You need to be Successful

Suppose you have got an opportunity to create a project on AI. Try implementing these five before the start. These five are Learning, Programming Language, Knowledge representation, Problem Solving, and Hardware. Ensure These 5 Things Done, if you want to be your AI Project Successful 1. Learning Process. What is learning? - adding knowledge to the storehouse, and improving its performance. The success of an AI program depends on two things- the extent of wisdom it has and how frequently it acquires it. Learning agents consist of four main components. They are the: The Learning element - is part of the agent responsible for improving its performance.  The Performance element - is the part that chooses the actions to take.  Critics , that tell the learning element of how the agent is doing.  The Problem generator - suggests actions that could lead to new information experiences. 2. Programming Language. LISP and Prolog are the primary languages used in AI programming. LISP (List Pro

How to Show Data Science Project in Resume Correctly

The data scientist resume should have mentioned the project correctly. Here are my ideas on how to show the project in the resume. How to Show Data Science Project? 1. Preparation of Resume .  The first step for an interview for any project is you need Resume. You need to tell clearly about your resume.  2. Answering about Project.   In interviews, you will be asked questions about your project. So the second step is you need to be in a position to explain the project. 3. Answering your Project Role.  The third point is you need to explain the roles you performed in your data science project. If you mention the roles correctly, then, you will have a 100% chance to shortlist your resume. Based on your experience your resume can be 1 page or 2 pages .  4. Technologies in the Resume. In interviews, again they will be asked how you used different tools to complete your data science project. So, you need to be in a position to explain how you used different options present in the tools.  S

Data science: Simple Project to Practice

I want to share with you how to use Python for your Data science or analytics Projects. Many programmers struggle to learn Data science because they do not know where to start. You can get hands-on if you start with a mini-project. I have used Ubuntu Operating System for this project. You Need dual skills; Learning and Apply knowledge to become a data scientist. In Data science you need to learn and apply your knowledge.   After engineering, you can go for M Tech Degree.  You can become a real engineer if you apply engineering principles. So Data science also the same. Data Visualization in Python is my simple project Importance of Data Data is a precious resource in resolving Machine Learning and Data Science Problems.  Define first what is your problem. Collect Data  Wrangle the Data and Clean it. Visualize the Patterns In the olden days , you might be studied a subject called Statistical Analysis.  In this subject, you need to study the actual problem and collect the data in

Python Advanced For Loop With Example

Basically, For Loop reads input value and stores in a variable. So For Loop in Python needs two arguments. The two arguments are VARIABLE and IN. So far so good. The next part of the article and examples well explained how it works and how to get the index for each input supplied.

IoT application project need for BTech ECE students

IOT Project IoT provides networking to connect people, things, applications, and data through the Internet to enable remote control, management, and interactive integrated services. IoT network scale, how large is it? Well, you have to think of this. The number of mobile devices exceed the number of people on Earth. In addition, predictions are made that there will be 50 billion 'things' connected to the Internet by 2020. So therefore, Internet of Things, this study is so important.  IoT Service Support. Some advanced IoT services will need to collect, analyze, and process segments of raw sensor information, raw sensor data, and we need to turn this into operational control information. Some sensor data types may have massive sizes, because the number of sensor IoT devices are so large.  #Introduction to Elecronic circuit analysis Also Read :   Project and Training on Io T So we need a platform that can collect and store all of this massive amount of