I have created Job ready Data Analyst Course in 90 Days which involves analysis in several platforms like Excel, Python, R and SQL along with Visualization in Tableau.

The Google Advanced Data Analytics Certificate contains information on how to use machine learning, predictive modeling, and experimental design to collect and analyze large amounts of data, and prepare for jobs like Senior Data Analyst and Junior Data Scientist.

Data Analyst Job Interview Practice - Questions Answering

Decentralized Finance (DeFi) Development Services & Solutions Eliminate the role of a middleman by availing decentralized finance (DEFI) development services & solutions. Get access to the major financial services through a blockchain network and experience the benefits of automation, a higher level of security, anonymity, interoperability, and transparency. Our wide range of services include Market-Making Consulting We take immense efforts in establishing financial markets that understand the customers’ proprietary algorithms. We aim at improving the access of liquidity to investors and democratize the whole system. We render customized features according to the customer’s expected return on investment. Decentralized Crypto Banking We ensure a frictionless user experience by facilitating the direct transfer of value between the involved parties supported by decentralization. Our ready-to-launch white-label mobile payment apps render a variety of services such as wallet integration, value holding, and detailed transactional analysis. Defi Lottery System Development We provide a no-loss lottery system that benefits our participants completely. We take steps to eliminate the custodianship of the pooled capital. We permit investing your capital in other related dapps and distribute the rewards in form of a major share of the interest earned to a winner randomly selected by the smart contracts. We assure the regular flow of returns. Derivatives Over Defi Platform We ensure seamless access to derivatives and maximize your earning potential by many notches. by establishing robust dapps, we enable traders to hedge their portfolio of investments and minimize risks by directly engaging with their peers through a democratic platform. We are experts in derivatives market-making and Dapp platform development. Decentralized Fund Management All your crypto assets will be managed to yield high performance in a decentralized exchange through smart control and management. with in-depth experience in investment exchanges along with our strong knowledge of defi, we render our services at low fees and avoid potential risks. Defi Insurance System Development We ensure that there are no risks present in our smart contract. With our robust provision of insurance services, we assure you that there will be no chance of uncontrollable liquidity requests. We contain futuristic risks, uncertainties, and emergencies through lucrative insurance deals. Defi Yield Farming Platform Development Yield farming refers to the technique through which one can earn more cryptocurrencies by using his existing holding of cryptos. Liquidity providers play a vital role in the success of yield farming. They stake their assets in liquidity pools and facilitate trading in cryptos by creating a market. Defi Staking Platform Development Defi staking involves a mechanism where crypto assets will be staked on a supported wallet or exchange and passive income will be earned. The rewards can be calculated based on the quantity of staked assets, the staking duration, inflation rate, and the network issuance rate. Defi Lending Platform Development Defi lending platforms have been made popular by the likes of aave and compound. The basic features of a defi lending platform include flash loan facilities, a fiat payment gateway, and an exclusive margin trading facility, the advantages of defi lending include high immutability, better transparency, quick access, and resistance to transaction censorship. Defi Smart Contract Development One of the pivotal reasons behind the tremendous growth of defi services is due to the heavy investments made in robust defi smart contract development. They are created with the solidity programming language, highly encrypted, and automates the tasks to be executed based on certain pre-set terms and conditions. Defi Dapp Development Defi Dapp development plays a critical role to avoid the risk of a central point of failure. They are highly secure when compared to centralized applications due to the absence of a central authority. Defi Tokens Development Defi tokens development has played a critical role in boosting the growth of decentralized applications. Their value is currently higher than bitcoin. it has a huge trading volume and has garnered a lot of attention from the mainstream crowd in recent times. Defi Dex Development Like Uniswap Uniswap is one of the leading defi projects being undertaken. It is an innovative venture as it utilizes incentivized liquidity pools instead of regular order books. every user of uni swap will is rewarded with a percentage of fees incurred on every ethereum transaction for rendering liquidity to the system. Defi Wallet Development Traders will have complete control over their funds through defi wallet development without the interference of any authorities in the system. Supreme security is guaranteed for users without any compromise. By supplying customized private keys to every user, there will not be any chances for any loss of data. DeFi Marketing Services To assist DeFi projects gain user engagement, marketing services are indispensable.From drafting white paper, video and content marketing, to legal advisory, marketing and community management, our DeFi marketing and consulting services are well-versed to get the job done. DeFi Synthetic Asset Development Synthetic assets derive their value from underlying assets and derivatives which are essentially smart contracts. In DeFi, Synthetic assets have gained acclaim as they involve low risks and little chance of price fluctuations. Users can easily invest, trade, and own assets with no hassles. DeFi Solutions For Ecommerce Streamline your Ecommerce business with DeFi and its pragmatic tools. With DeFi’s solutions , benefits like omission of intermediaries, faster shipping, supply chain management, and real time tracking can be integrated with your Ecommerce business, increasing profits. DeFi Tokenization Development Tokenization Development is one of the pragmatic solutions DeFi offers. Users can now convert inoperative and underutilized assets into great profits by simply tokenizing their assets. With our DeFi tokenization, avail of ERC20, ERC721 & NFT tokens for your assets. DeFi Crowdfunding Platform Development Although a relatively new sector, DeFi crowdfunding has become the go-to mode of aggregating funds to support businesses and start-ups. Our DeFi Crowdfunding platform services come with additional benefits in the likes of tax benefits, instant approval, fundraising calendars and more. DeFi Real Estate Platform Development DeFi has revolutionized the ways of real estate management. Now real estate owners and investors, with the help of blockchain based tokens, can make property investment seamless and manageable. With fractional ownership, financial inclusivity is now possible. DeFi ICO Development One of the leading fundraising methods, DeFi ICO services are distinguished. Creating utile tokens, community management, escalating coin value, and launching projects with diligence & guidance from market analysts and blockchain experts is inclusive of our ICO Development. DeFi Exchange Development Offering users a plethora of apparent benefits, DEXs are the prized innovation of DeFi. Offering high-end security, durable liquidity, complete anonymity and financial inclusivity, DEXs make trading and transacting crypto accessible and lucrative for crypto enthusiasts. DeFi Protocol Like Yearn. Finance Yearn. Finance offers the best APY the market has to offer by referring to popular exchanges. This protocol offers its users the best yields in a highly secure network. With in-built smart contracts and an open source code, it supports a range of Stablecoins offering huge returns. DeFi Protocol Like AAve The DeFi protocol Aave offers crypto traders a robust platform for lending and borrowing of crypto for which they earn high interests. The highlight feature of Aave - Flash loans and flexible interest rates make it a profitable platform for crypto traders. DeFi Exchange Like 1inch 1inch exchange now has the reputation of being the DEX offering users the lowest slippage. As an aggregator, 1inch connects several exchanges to one platform in a non-custodial ecosystem. With governance and farming features, trading on 1inch remains prominent.

A PostgreSQL project using a dataset that pulls job postings from Google's search results for Data Analyst positions in the United States. Dataset created by Luke Barousse.

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. Day:1 In this project, Students will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. They will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. Technologies that will be covered are Numpy, Pandas, Matplotlib, Seaborn, Jupyter notebook. We will be giving the students a deep dive into the Data Analytical process Day:2 We will be giving the students an insight into one of the major fields of Machine Learning ie. Time Series forcasting we will be taking them through the relevant theory and make them understand of the importance and different techniques that are available to deal with it. After that we will be working hands on the bike share data set implementing different algorithms and understanding them to the core We aim to provide students an insight into what exactly is the job of a data analyst and get them familiarise to how does the entire data analysis process work. The session will be hosted by Shaurya Sinha a data analyst at Jio and Parag Mittal Software engineer at Microsoft.

(Deprecated) Task for the Search & Discovery data analyst job.

Scraped job description and leveraged the concepts of Natural Language Processing (NLP) and GloVe Algorithm to extract the keywords through data and performed analysis. Presenting the vital keywords from data analyst job summary from the Indeed website..

NYC Tech Jobs is the City's initiative to highlight critical roles for developers, data analysts, and designers in City government.

Gain the job-ready skills for an entry-level data analyst role through this eight-course Professional Certificate from IBM and position yourself competitively in the thriving job market for data analysts, which will see a 20% growth until 2028 (U.S. Bureau of Labor Statistics). Power your data analyst career by learning the core principles of data analysis and gaining hands-on skills practice. You’ll work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics, gaining practical experience with data manipulation and applying analytical techniques.

For this project, you will assume the role of a Data Scientist / Data Analyst working for a new startup investment firm that helps customers invest their money in stocks. Your job is to extract financial data like historical share price and quarterly revenue reportings from various sources using Python libraries and webscraping on popular stocks. After collecting this data you will visualize it in a dashboard to identify patterns or trends. The stocks we will work with are Tesla, Amazon, AMD, and GameStop. Dashboard Analytics Displayed A dashboard often provides a view of key performance indicators in a clear way. Analyzing a data set and extracting key performance indicators will be practiced. Prompts will be used to support learning in accessing and displaying data in dashboards. Learning how to display key performance indicators on a dashboard will be included in this assignment. We will be using Plotly in this course for data visualization and is not a requirement to take this course. Watson Studio In the Python for Data Science, AI and Development course you utilized Skills Network Labs for hands-on labs. For this project you will use Skills Network Labs and Watson Studio. Skills Network Labs is a sandbox environment for learning and completing labs in courses. Whereas Watson Studio, a component of IBM Cloud Pak for Data, is a suite of tools and a collaborative environment for data scientists, data analysts, AI and machine learning engineers and domain experts to develop and deploy your projects. Review criteria There are two hands-on labs on Extracting Stock Data and one assignment to complete. You will be judged by completing two quizzes and one peer review assignment. The quizzes will test you based on the output of the hands-on labs. In the peer review assignment you will share and take screen shots of the outcomes of your assignment.

Using web scraping, NLP and Neural Networks on Linkedin data to build an AI that can identify different data job descriptions. (Data Scientist, Data Engineer and Data Analyst).

This project scrapes data jobs(data engineer, analysts and science) from LinkedIn and stores the data in MySql then a streamlit app integrated with openai that writes about me automatically from job description

# Pandas Homework - Pandas, Pandas, Pandas ## Background The data dive continues! Now, it's time to take what you've learned about Python Pandas and apply it to new situations. For this assignment, you'll need to complete **one of two** (not both) Data Challenges. Once again, which challenge you take on is your choice. Just be sure to give it your all -- as the skills you hone will become powerful tools in your data analytics tool belt. ### Before You Begin 1. Create a new repository for this project called `pandas-challenge`. **Do not add this homework to an existing repository**. 2. Clone the new repository to your computer. 3. Inside your local git repository, create a directory for the Pandas Challenge you choose. Use folder names corresponding to the challenges: **HeroesOfPymoli** or **PyCitySchools**. 4. Add your Jupyter notebook to this folder. This will be the main script to run for analysis. 5. Push the above changes to GitHub or GitLab. ## Option 1: Heroes of Pymoli  Congratulations! After a lot of hard work in the data munging mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned the task of analyzing the data for their most recent fantasy game Heroes of Pymoli. Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights. Your final report should include each of the following: ### Player Count * Total Number of Players ### Purchasing Analysis (Total) * Number of Unique Items * Average Purchase Price * Total Number of Purchases * Total Revenue ### Gender Demographics * Percentage and Count of Male Players * Percentage and Count of Female Players * Percentage and Count of Other / Non-Disclosed ### Purchasing Analysis (Gender) * The below each broken by gender * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Gender ### Age Demographics * The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.) * Purchase Count * Average Purchase Price * Total Purchase Value * Average Purchase Total per Person by Age Group ### Top Spenders * Identify the the top 5 spenders in the game by total purchase value, then list (in a table): * SN * Purchase Count * Average Purchase Price * Total Purchase Value ### Most Popular Items * Identify the 5 most popular items by purchase count, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value ### Most Profitable Items * Identify the 5 most profitable items by total purchase value, then list (in a table): * Item ID * Item Name * Purchase Count * Item Price * Total Purchase Value As final considerations: * You must use the Pandas Library and the Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of three observable trends based on the data. * See [Example Solution](HeroesOfPymoli/HeroesOfPymoli_starter.ipynb) for a reference on expected format. ## Option 2: PyCitySchools  Well done! Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. In your latest role, you've become the Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities. As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your responsibility is to aggregate the data to and showcase obvious trends in school performance. Your final report should include each of the following: ### District Summary * Create a high level snapshot (in table form) of the district's key metrics, including: * Total Schools * Total Students * Total Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### School Summary * Create an overview table that summarizes key metrics about each school, including: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Top Performing Schools (By % Overall Passing) * Create a table that highlights the top 5 performing schools based on % Overall Passing. Include: * School Name * School Type * Total Students * Total School Budget * Per Student Budget * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Bottom Performing Schools (By % Overall Passing) * Create a table that highlights the bottom 5 performing schools based on % Overall Passing. Include all of the same metrics as above. ### Math Scores by Grade\*\* * Create a table that lists the average Math Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Reading Scores by Grade * Create a table that lists the average Reading Score for students of each grade level (9th, 10th, 11th, 12th) at each school. ### Scores by School Spending * Create a table that breaks down school performances based on average Spending Ranges (Per Student). Use 4 reasonable bins to group school spending. Include in the table each of the following: * Average Math Score * Average Reading Score * % Passing Math (The percentage of students that passed math.) * % Passing Reading (The percentage of students that passed reading.) * % Overall Passing (The percentage of students that passed math **and** reading.) ### Scores by School Size * Repeat the above breakdown, but this time group schools based on a reasonable approximation of school size (Small, Medium, Large). ### Scores by School Type * Repeat the above breakdown, but this time group schools based on school type (Charter vs. District). As final considerations: * Use the pandas library and Jupyter Notebook. * You must submit a link to your Jupyter Notebook with the viewable Data Frames. * You must include a written description of at least two observable trends based on the data. * See [Example Solution](PyCitySchools/PyCitySchools_starter.ipynb) for a reference on the expected format. ## Hints and Considerations * These are challenging activities for a number of reasons. For one, these activities will require you to analyze thousands of records. Hacking through the data to look for obvious trends in Excel is just not a feasible option. The size of the data may seem daunting, but pandas will allow you to efficiently parse through it. * Second, these activities will also challenge you by requiring you to learn on your feet. Don't fool yourself into thinking: "I need to study pandas more closely before diving in." Get the basic gist of the library and then _immediately_ get to work. When facing a daunting task, it's easy to think: "I'm just not ready to tackle it yet." But that's the surest way to never succeed. Learning to program requires one to constantly tinker, experiment, and learn on the fly. You are doing exactly the _right_ thing, if you find yourself constantly practicing Google-Fu and diving into documentation. There is just no way (or reason) to try and memorize it all. Online references are available for you to use when you need them. So use them! * Take each of these tasks one at a time. Begin your work, answering the basic questions: "How do I import the data?" "How do I convert the data into a DataFrame?" "How do I build the first table?" Don't get intimidated by the number of asks. Many of them are repetitive in nature with just a few tweaks. Be persistent and creative! * Expect these exercises to take time! Don't get discouraged if you find yourself spending hours initially with little progress. Force yourself to deal with the discomfort of not knowing and forge ahead. Consider these hours an investment in your future! * As always, feel encouraged to work in groups and get help from your TAs and Instructor. Just remember, true success comes from mastery and _not_ a completed homework assignment. So challenge yourself to truly succeed! ### Copyright Trilogy Education Services © 2019. All Rights Reserved.

The text emphasizes the importance of continuous learning, strategic job searching, and a focus on in-demand skills for job seekers. Employers are encouraged to create a competitive and inclusive environment, support employee growth, and utilize effective recruitment strategies to attract and retain top talent.

Problem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.

I am developing an algorithm to classify jobs in the data field as: Data Scientist, Data Engineer, Data Analyst, or Machine Learning Engineer. I want to create a list of common words and phrases from each job posting so that applicants like myself can utilize them in creating resumes and cover letters that stand out to potential employers!

⚡️ The fastest job aggregator for New Grads in the US. Updated hourly. Find Entry Level Software Engineer, Data Analyst, and Product Management roles before they expire. Visa Sponsorship info included.

Data Analyst job posting analysis

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Analysis of 2,250+ data analyst job postings using Python and Tableau

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"📊 🇺🇸 Explore Data Analyst job trends across the USA with SQL! Uncover top-paying jobs, in-demand skills, and key market trends in the Data Analyst field.

A streamlined n8n workflow for automating job searches on LinkedIn and Indeed (via Adzuna), targeting roles like AI Engineer, ML Engineer, Python Developer, Data Scientist, Cybersecurity Analyst, Security Engineer, and Threat Intelligence Specialist.

Insights on skills and salaries using real data (scraped from Linkedin) - https://rafabelokurows.github.io/data-analyst-job-skills/

Analysis of data analyst job postings from 2023 that uncovers key insights about the global and German data analysis job markets

The objective of this project is to scrape data for jobs site to analyze and rank the top skill employer our seeking to fill Data Analysis job positions.

LinkedIn Data Analyst Jobs Visualization Project

I analysed the data analyst jobs data set to find the some insights such as the most in demand data anlyst jobs, most competitive, and so on

Python Script to Scrape All Data Analyst Job data Posted on Wuzzuf Website