Improvement as Data Analyst presents business problems, different problem-solving tools (5 Why, Action Priority Chart, Fishbone, and Flow Mapping), and data analysis process.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data.
Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques.
Key Takeaways:
(i) Demystify the myths of analytics
(ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars.
(iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Data science in demand planning - when the machine is not enoughTristan Wiggill
A presentation by Calven van der Byl BCom Economics and Statistics, BCom Honours Mathematical Statistics, Masters Mathematical Statistics, Inventory Optimization Demand Planning Manager, DSV, South Africa.
Delivered during SAPICS 2016, a leading event for supply chain professionals, held in Sun City, South Africa.
Demand Planning is a complex, yet often de-emphasized function in the supply chain planning function. The demand planning function is often characterized by an over-reliance on off the shelf software as well as a great deal of manual intervention. This presentation will outline the current developments and perspective in big data analytics and how they can be leveraged with the demand planning function to improve forecasting agility and efficiency. A simulation study will be presented in order to illustrate these principles in practice.
Data Analyst Interview Questions & AnswersSatyam Jaiswal
Practice Best Data Analyst Interview Questions for the best preparation of the data analyst interview. these interview questions are very popular and asked various times in data analyst interview.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
This dashboard aims to evaluate the monthly sales achievement per month for mobiles and tablets, computing, and appliances in a superstore. The tool that is used in this project is Looker Studio.
This project focused on creating data frames, filtering data, grouping data, merging, and displaying data. Furthermore, it also includes creating new columns in which specific conditions can be applied. The data is used to solve business problems within a superstore.
The first problem statement is determining the prizes taken from the Top 5 products from the Mobiles & Tablet Category. Second, the data is processed to fulfill the requirement to check whether there is a decrease in the sales of the Others Category in 2022. The task also requires the display of the top 20 products that have the highest decrease. Third, I utilize the data to process the Customer ID and Registered Data of the consumers who have checked out but have not yet made payment. Fourth, the data is sorted and analyzed to compare the average daily sales on the weekends and those on the weekdays in the time range of 3 months.
In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data.
Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques.
Key Takeaways:
(i) Demystify the myths of analytics
(ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars.
(iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Data science in demand planning - when the machine is not enoughTristan Wiggill
A presentation by Calven van der Byl BCom Economics and Statistics, BCom Honours Mathematical Statistics, Masters Mathematical Statistics, Inventory Optimization Demand Planning Manager, DSV, South Africa.
Delivered during SAPICS 2016, a leading event for supply chain professionals, held in Sun City, South Africa.
Demand Planning is a complex, yet often de-emphasized function in the supply chain planning function. The demand planning function is often characterized by an over-reliance on off the shelf software as well as a great deal of manual intervention. This presentation will outline the current developments and perspective in big data analytics and how they can be leveraged with the demand planning function to improve forecasting agility and efficiency. A simulation study will be presented in order to illustrate these principles in practice.
Data Analyst Interview Questions & AnswersSatyam Jaiswal
Practice Best Data Analyst Interview Questions for the best preparation of the data analyst interview. these interview questions are very popular and asked various times in data analyst interview.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
This dashboard aims to evaluate the monthly sales achievement per month for mobiles and tablets, computing, and appliances in a superstore. The tool that is used in this project is Looker Studio.
This project focused on creating data frames, filtering data, grouping data, merging, and displaying data. Furthermore, it also includes creating new columns in which specific conditions can be applied. The data is used to solve business problems within a superstore.
The first problem statement is determining the prizes taken from the Top 5 products from the Mobiles & Tablet Category. Second, the data is processed to fulfill the requirement to check whether there is a decrease in the sales of the Others Category in 2022. The task also requires the display of the top 20 products that have the highest decrease. Third, I utilize the data to process the Customer ID and Registered Data of the consumers who have checked out but have not yet made payment. Fourth, the data is sorted and analyzed to compare the average daily sales on the weekends and those on the weekdays in the time range of 3 months.
This final project presents my analysis of sales and methods of payment for electronics, fashions, entertainment, and other products offered by a superstore. I used many SQL commands including Create Table, Select, From, Where, Group By, Order By, Limit, Left Join, and Extract.
This task presents SQL basic commands which I used to create a new table with its data. I queried the sales data of furniture, office supplies, and technology.
Through my task, I learned about how to work with the Google Sheet. This task covers data extraction, number formatting, conditional formatting, how to remove duplicate data, and data validation. The data presents the sales and consumer segment of office supplies, furniture, and technology in the United States.
This work explains the Basic Statistics for Data Analysis which includes the type of data, measure of centric (mean, median, etc.), measure of distribution (variance, deviation standard), quartile, percentile, and outliers. In this task, I used statistics to analyze voucher redeems, the service-level agreements, and compare payment with living costs.
First Session - Kickstart Career as Data Analyst presents the definition of data, 5 parameters of big data, why many companies today need data, and different data-related jobs including data engineer, data analyst, and data scientist.
Hello everyone! This is my Excel Portfolio which covers all of my tasks during my intensive bootcamp. Through this bootcamp, I learned about Basic Formula and Functions, Data Cleaning, Data Validation, Conditional Formatting, Data Visualization, VLookup and Match, Pivot, Dashboard Reporting, and Macro VBA. This portfolio presents my analysis of sales, revenue, profit, and popular marketplace of office supplies and furniture. Hopefully, this will help me to open my career path.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. Data Analyst
Oleh:
Improvement as Data
Analyst
JOIN THE BEST UPSKILLING COMMUNITY
WITH ME at myskill.id/bootcamp
FULLSTACK INTENSIVE BOOTCAMP
MINI PORTOFOLIO
Elyada Wigati Pramaresti
2. #RintisKarirImpian
Course Summary
Topics Summary
Data Analysis Fundamental • Data analytics is the concept and practice of all activities related to
data.
• Data analysis is the process of data collection, data cleaning,
transformation, data visualization, and data modeling to help
decision-making.
• Data analysis can be a validation of certain information.
• The contribution of data analysis:
- Creates a better decision
- Lessens the business’ risks
- Increases transparency and objectivity
- Improves business control
3. Topics Summary
Data Analysis
Fundamental
• Most popular tools for data analysis:
1. SQL
- Used every day by data analyst
- Used by data analysts to interact with data in the database
2. Python
- Applied to process big data
- Create statistical models and machine learning
3. BI Tools
- Used to create a dashboard for data visualization
Understanding Business
Problem
• General steps of problem-solving:
1. Understand the hypothetical factors and context
2. Determine the stakeholders to be asked for information
3. Create the framework to determine causal factors
4. Topics Summary
Understanding Business
Problem
• Problem solving tools:
1. 5 Why
Repeatedly asking about the cause of the problems until
an objective, clear, and right answer is obtained
2. Action priority chart
It is used to prioritize the problems based on their impacts and
benefits to the organization’s goal.
3. Fishbone diagram
It is used to seek and explain the root causes of different
point of views
4. Flowchart/Algo
Creates pseudo algorithms to determine the problems and
systematically seek the solutions
9. Topics Summary
Data Analysis Process • Plan = Identify the problem and make some hypothesis
• Do = Testing the hypothesis
• Check = Analyze the test result
• Act = Implementing the suitable new standard
10. #RintisKarirImpian
Case Study
Sebuah perusahaan telekomunikasi yang ada di
Indonesia ingin meningkatkan retensi
pelanggan. Tentu saja mereka membutuhkan
seorang data analyst untuk mengetahui dan
memahami pola perilaku pelanggan lainnya
yang melakukan retensi. Analisalah masalah ini
menggunakan pendekatan PDCA.
11. #RintisKarirImpian
Framework PDCA
Plan Do Check Action for Data Analyst
Plan Do
Action
Check
Planning by identifying
the problems and making
some hypothesis
Applying the plan
through testing
Evaluate the result to
prevent repeated errors
Applying the new
business standards and
monitoring the results
12. #RintisKarirImpian
Analysis using PDCA Framework
PDCA Analysis
Plan • Plan the objective for increasing retention and target its escalation number.
• Determine the problems and create some hypotheses about the factors that
affect the customers’ retention like customer service, price, product quality, and
promotion
• Collecting the required data for the testing and analysis. These include the
customers’ data like age, gender, and residency; the transaction data such as
transaction date and how many items per purchase; the number of repeated
purchases; and the customers; satisfaction data.
• Planning for the hypothesis verification.
13. #RintisKarirImpian
Analysis using PDCA Framework
PDCA Analysis
Do • Carrying out the data collection. It can be obtained from the company’s
database or by applying questionnaires to get the new data that is not yet
stored in the database.
• Screening the data to identify their structure and quality.
• Processing the data by cleaning the invalid data. This is necessary to
prevent bias during the checking process. The data analyst can also
combine the relevant data from other sources.
14. #RintisKarirImpian
Analysis using PDCA Framework
PDCA Analysis
Check • Implementing statistical methods to create predictive models. In this phase,
the data analysts assess the behavior patterns of the consumers and identify
the relevant variables that affect their behaviors.
• Evaluate the predictive models by using evaluation metrics such as accuracy
and F1 score.
• Assess whether the results meet the retention target.
15. #RintisKarirImpian
Analysis using PDCA Framework
PDCA Analisis Kamu
Act • Giving recommendations to the users. These can be adjusted pricing, product
quality improvement, improving customer service, and strategic product
promotion.
• Carry out monitoring to see the new standard implementation results in the
customers’ retention. See if the results meet the desired target.
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Instagram : elyadawigatip
Twitter : @EliNoBishamon
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wigati-pramaresti-1a2387170/
Bootcamp Data Analysis
by @myskill.id
