#data analyst course

[midmweb]

Data Analysis Course in Delhi

Introduction to Data Analysis

In today’s data-driven world, the ability to analyze and interpret data is becoming increasingly valuable. Data analysis involves inspecting, cleaning, transforming, and modeling data to uncover meaningful insights and inform decision-making processes. Whether it’s in business, healthcare, finance, or any other field, the demand for skilled data analysts is on the rise.

In today’s digital age, the ability to interpret and derive meaningful insights from data sets is highly valued. As the demand for skilled data analysts continues to grow, enrolling in a comprehensive data analysis course becomes essential for individuals looking to excel in this field.

The Need for Data Analysis Courses

With the exponential growth of data, there is a corresponding increase in the need for professionals who can extract actionable insights from it. This has led to a surge in demand for data analysis courses. These courses provide individuals with the skills and knowledge necessary to excel in this rapidly evolving field.

The Data Analysis Course plays a very very important role in various fields, including business, science, finance, healthcare, and more.

Overview of Modulation Digital (MIDM)

Modulation Digital is a leading institute that offers comprehensive courses in data analysis. With a focus on providing practical, hands-on training, Modulation Digital equips students with the skills they need to succeed in the world of data analysis.

Data Analysis Course at Modulation Digital (MIDM)

The data analysis course in delhi at Modulation Digital covers a wide range of topics, including data visualization, statistical analysis, machine learning, and more. Taught by industry experts with years of experience, the course provides students with the knowledge and tools they need to excel in this field.

Data Collection: Gathering relevant data from various sources. This can include databases, surveys, sensors, social media, and other data repositories.

Data Cleaning and Preprocessing: Addressing issues such as missing values, outliers, and inconsistencies in the data to ensure its accuracy and reliability.

Exploratory Data Analysis (EDA): Examining and visualizing the dataset to understand its characteristics, identify patterns, and uncover insights. EDA often involves creating charts, graphs, and summary statistics.

Descriptive Statistics: Calculating basic statistical measures like mean, median, mode, standard deviation, and other metrics to summarize and describe the main features of a dataset.

Inferential Statistics: Making inferences and predictions about a population based on a sample of data. Hypothesis testing and confidence intervals are common techniques in inferential statistics.

Data Visualization: Representing data graphically using charts, graphs, and other visual elements to make complex patterns and trends more understandable.

Descriptive Statistics: Calculating basic statistical measures like mean, median, mode, standard deviation, and other metrics to summarize and describe the main features of a dataset.

Statistical Analysis: Applying statistical methods to analyze relationships between variables, test hypothesis, and make predictions about future observations.

Benefits of Taking a Data Analysis Course

Enrolling in a data analysis course offers numerous benefits. From increased job opportunities to higher earning potential, the skills gained through these courses are highly sought after by employers across industries.

Machine Learning: Utilizing algorithms and models to automatically learn patterns from data, make predictions, or classify information. Machine learning is often used for tasks such as regression, classification, clustering, and recommendation systems.

Big Data Analytics: Handling and analyzing large volumes of data that traditional databases may struggle to manage. Technologies like Hadoop and Spark are commonly employed in big Data Aanalytics.

Text Analysis and Natural Language Processing (NLP): Analyzing unstructured text data to extract insights, sentiment, and patterns. NLP techniques are used for tasks like text classification, sentiment analysis, and information extraction.

Predictive Modeling: Building models that use historical data to make predictions about future events. This involves selecting and training a model on a dataset and then using it to make predictions on new, unseen data.

Data Mining: Exploring large datasets to discover hidden patterns, relationships, or trends. Data mining techniques include clustering, association rule mining, and anomaly detection.

Real-time Analytics: Analyzing data as it is generated or received, allowing for immediate decision-making. Real-time analytics is crucial in applications where timely insights are essential.

Interpretation and Communication: Interpreting the results of the analysis and communicating findings to stakeholders. This often involves creating reports, dashboards, and presentations.

Why Choose Modulation Digital (MIDM) for Data Analysis Course?

Modulation Digital stands out from other institutes due to its comprehensive curriculum, experienced faculty, and track record of success. With a focus on practical, hands-on learning, Modulation Digital ensures that students are well-prepared for the challenges of the real world.

Interdisciplinary Expertise: Strength your foundational Knowledge and apply this understanding to specific problems, get versatile training in data-sentric research, and build competencies who work in multiple programming in environment.

Strong Acadmeic Support: Learn From a variety of Activities and through high degree of Communication Between Faculty and Students.

Self-paced Learning: Engage and learn at your own faq. Explore our extensive e-libraries, access your learning materials online anytime, anywhere attend live and Recorded lectures and talks that works for your schedule and engage in discussion on our platforms. access study material even on low bandwidth internet on an advanced digital platform that can be used on your phone, laptop, or desktop.

Industry-Oriented Curriculum: Gain real world insights from industry focussed modules and projects. learn from faculty who come with decodes of rich industry experience and expertise and interact with mentress & faculty to understand industry specific challenges and Oppurtunities.

In Conclusion:

Enrolling in a data analysis course in delhi at Modulation Digital is a smart investment in your future. With a comprehensive curriculum, experienced faculty, and a track record of success, Modulation Digital prepares students for rewarding careers in data analysis.

Modulation Digital is recognized for its quality and has gained accreditation for many of its programs. Modulation Digital certificates are widely accepted by governments, corporate organizations, and higher education, institutes for jobs in higher education, future prospectus.

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[beezelbubbles]

Okay. I don't know what to do anymore. Every choice seems bad. (Probably because I need to refill my antidepressants and ADHD meds but you know, whatever.)

I keep getting ads for a data analyst boot camp. IDK if I should go for it. I think I can manage the work. I'm a little wary of the math involved. I'm alright at math, but I don't super enjoy it. I do enjoy problem solving. But freelance editing is not paying my bills.

So Tumblr nerds and adults. Do I give this a shot? It's about 13 grand for the program, which also involves several job fairs and career counseling. But I'm worried about this being one of those jobs that isn't going to exist fairly soon because of AI and general layoffs in the tech sector.

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[anishmary]

Exploring the World of Data Analytics: Opportunities and Beyond

Describe data analytics. Analyzing data to find useful information, patterns, and trends is known as data analytics. Businesses adore it since it aids in their improvement. It’s similar to solving problems with data.

Why is data analysis so crucial? Consider yourself in possession of a treasure map (data) and the desire to locate the treasure (insights). Your compass is data analytics.

Why it matters is as follows: It aids in your understanding of consumer behavior, market trends, and company performance.

Efficiency: You can discover areas in which your company may save both time and money.Smart decisions: When using data, you may base decisions on facts rather than educated guesswork.

Personalization: Have you ever seen how Netflix suggests shows? That is data analysis.

Future Scope of Data Analytics :

High Demand: Many businesses in different industries need data experts because they use data to make smart choices. So, the need for skilled data analysts is growing.

Various Job Fields: Data skills are useful everywhere, not just in one industry. You can work in finance, healthcare, e-commerce, marketing, or technology. This means you have many options for your career.

Tech Keeps Changing: The tools and methods for working with data keep getting better and different. It’s important to keep learning new things, but it also means you can become an expert in a special area.

Helping Decisions: Data people play a big role in helping businesses make important decisions. What you find in data can change how a company plans for the future.

Career Growth: Starting as a data analyst can lead to better and higher-paying jobs like data scientist or machine learning engineer.

Good Pay: Data analysts are paid well. As you get better at your job, you can earn even more.

Job Security: As long as companies use data, they’ll need people who understand it. So, you’ll have a stable job.

Work from Anywhere: You can often do data work from anywhere, giving you flexibility in where you live.

Ethical Data: Data privacy and being fair with data are important. People who can make sure data is used the right way are in demand.

Worldwide Opportunities: Data skills are needed all over the world, so you

can work in different countries if you want.

Careers in Data Analytics:

Data Analyst: The entry point They collect, clean, and analyze data.

Data Scientist: The Experts They use machine learning to solve complex puzzles.

Business Analyst: The bridge between data and business decisions

Big Data Engineer: They manage massive data sets using big data tools.

Healthcare Analyst: They use data to improve healthcare outcomes.

Marketing Analyst: Masters of measuring marketing success

Financial Analyst: They use data for financial strategies.

Sports Analyst: They play with data in the world of sports.

The Future of Data Analytics:

AI and machine learning: Think smarter, not harder. Automation will rule.

IoT Analytics: Connected devices will provide more data than ever.

Ethical Analytics: Data privacy will be a top concern.

Data Visualisation: Making data pretty and easy to understand will be big.

Feel free to get in touch with an ACTE institution if you want to learn more about Data Analytics because they provide certifications and possibilities for job placement. Teachers with experience can improve your learning. These services are available offline and online. Take things slowly and, if you’re interested, think about signing up for a course.

I hope I was able to effectively respond to your query. If it’s not, add it in the comments section. I still think I have a lot to learn.

Consider following me and giving this answer an upvote if you found it to be useful. This will motivate me to post more information on data analytics.

We appreciate you taking the time to read this and voting it up. Enjoy your day.

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[imarticus]

All About Data Science and Analytics Course by Imarticus Leaning | Data Science For Beginners

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[ridhisingla001]

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[onlineitsolutions1]

SQL server ,Power BI, Data Analyst ,MSBI Courses & Training

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[windwitchswork]

Went back to take this more seriously.

Nailed down a lot of things I overlooked the first time. Now onto the next unit.

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[analyticstraininghub]

How to Become a Data Analyst in 2022?-

In the past decade, data has become of prime importance. Organizations are investing heavily to ensure the maximum yield universalize of information from the firm’s database. The need for this extract has risen after the revolution in trade brought about by data analytics. Data Analytics has revolutionized the way the higher management or the owner of the business see’s the data. The insights gained post evaluation and analysis of data and showcasing the same in a visually appealing format or report have modified the approach to business and the campaigns that the firms run to push sales and improve the goodwill of the brand.

How to become a Data Analyst With No Experience?

Data Analytics is a path of untold possibilities and is expected to grow larger than ever before. Since the revolution of digitization of records has lowered the operating costs for companies. The digitized data is stored in huge data silos called databases either through an outsourced connection or through cloud servers whichever fits the need of the business or startup. Data Analytics helps in gaining insights that might be hidden inside the data.

The future of data analytics, in general, is democratization. We have come a long way from only the statisticians or only the number crunchers being able to work with data and then hand it over to the analysts. The term that has been buzzing around the conference rooms is self-service data analytics. Being able to answer the questions of our customers which they don’t even know makes it easily achievable by employing tools like Power BI & Tableau which make it accessible to anybody. These tools do a great job of integrating and implementing a lot of features that require no coding.

Real-time decision-making based on real-time data becomes possible by taking or utilizing some of these advanced data analytics tools which help the user to create a connection between artificial intelligence and machine learning. Data analytics enables the operators to take those complex problems/issues and break them down for business users to understand whilst keeping it simple. The way Power BI & Tableau can drive insights from any basic data set extracted from any database.

Everything around us is data and we just need ways to harness, understand, learn and make good choices based on data analytics. It is here to stay and the next big wave is how do we implement it so it stays forever and continues to expand.

A Step-by-Step Guide to Become a Data Analytic:-There are basic steps with which anyone can start a career as a Data Analyst: –

Get a bachelor’s degree in Math or Computer science with priority on statistical or analytical skills.

How to become a data analyst without a degree – The easiest way to do this is to master important data analytical skills.

opt for a certification course with Analytics Training Hub to start a data analyst learning path.

Get a job at an entry-level as a data analyst.

Earn a Master’s in Data Analytics.

What does a Data Analyst do?

The job profile of a data analyst entails multiple steps, starting from: –

Discover the problem or determine what the owner needs.

Do they need a dashboard, do they need reports, do they need to do some type of analysis on their product and give some type of recommendation?

When the analysts finally get the idea of what they need to do, they have to create a plan of action.

As to when will the user be getting this data and where is it coming from.

Often it can be the user’s job to communicate that to the team.

The next thing that the user would want to do is to collect the data.

Data can come from a ton of different sources so whether that is an SQL backup, a flat file, or an API.

After extraction, the analyst should be able to get all that data into one place.

Then as a user, you would need to work with your programmers to create an extract, transform and load (ETL) process.

So, the user is going to work with the programmer to get the data, and then both the user and the coder are going to create business rules to transform it for how the data analyst wants it to look in your system.

Then the operator loads the data and this can also be known as creating an ETL pipeline.

if you have data that’s going to be coming in either weekly or monthly the operator wouldn’t want to repeat this process manually every single time.

So, creating a pipeline is creating an automated process to bring that data, in the same way, every single time and that’s going to save you a lot of time.

The very last thing is aggregating your data which just means standardizing data and putting it all together instead of having it as separate sources.

the next step would be to clean the data  Data is always messy.

Sometimes they use three different date formats, people’s names are capitalized for absolutely no reason and sometimes somebody forgets to add the customer id. So, you can’t map the patient in your system.

The analyst needs to do all this because it makes the data a lot more usable for later processes and part of this is normalizing and standardizing the data so that when you do your visualizations or your reports later all the data looks the same that can be used in any part that you need to be used in.

The next thing that the user needs to do is set up the data for reports and visualizations and oftentimes the user achieves this is by creating views.

A view allows the operator to combine several tables into one and then choose a subset of that. A data that the user wants to use for the reports and visualizations and each view may need to be formatted differently based on what the operator is going to be using it for in the report or the visualization.

Last and foremost is creating the reports and along with automation of that process so that if the owner wants it every week or every month it can just generate the report from a stored procedure or a job that automatically sends it out with the latest data every week or month.

The user can also connect that data to a data visualization tool like Tableau, power bi, python, or R.

What is the future Data Analyst job?

As per leading data connoisseurs of the data industry, the job profile of a data analyst seems to hold an extremely promising prospect in the next coming decade or two. The data Analyst job is a stepping stone and may lead to many of the below-mentioned job profiles depending on your interests: –

Data engineers:

data engineer would create the platform and the data structure within which all the data from the users would be captured for example what items they buy that is in their cart currently and what is on their wish list they have to make sure that the captured data is stored in such a fashion that is not only well-organized but it’s also easily retrievable. They should be comfortable working with every data source and employ ETL queries to collate data from multiple data sources and then organize all of this data in data warehouses or databases so that colleagues in the company can make the best use of it. To become a data engineer you need to acquire knowledge of languages such as Python, Java, SQL, Hadoop, Spark, Ruby, and C++. Now all of these are not mandatory but they vary from company to company for the job profile of a data engineer.

Business Analysts:

Business analysts are expected to draw insights from the data which would directly impact business decisions. Business analysts are directly involved in day-to-day business activities and there are a lot of ad hoc analyses that business analyst is expected to do, for example in an e-commerce company a business analyst would help the marketing team identify the customer segments that require marketing or the best time to market a certain product or why the last marketing campaign failed and what to do in future to prevent such mistakes hence for a business analyst a good understanding of business data and statistics is essential.

The tools and languages that would be most commonly used by you as a business analyst would be Excel, SQL, power bi, and tableau. Job profile of a business analyst may also be known as a data visualizer or a business intelligence professional who’s are responsible for creating weekly dashboards to inform the management about weekly sales of different products, the average delivery time, or the number of daily cancellations of orders, etc.

Data scientists:

A data scientist is a rare gem that employs data that has been existing in the organization to design business-oriented machine learning models. As a starting point, a data scientist can go through the available data of the company to look at various buying patterns identify similar items on the website, and then create algorithms around the same so that the website can automatically endorse products to the users based on the navigation history purchase of the consumer. Now this solution has to be effective enough that it can predict future purchases in real-time for visitors of the website.

Data analysts are expected to perform a lot of unplanned analyses which can facilitate decision-making within an organization. Data scientists on the other hand not only perform ad hoc analysis and create prototypes but also create data products that make intelligent decisions by themselves and this is where machine learning becomes extremely critical. For example, the suggestion you get after you buy a particular item or based on the items that you have on your wish list are because of machine learning models built by a data scientist.

The requisite skill for a data scientist is knowledge of algorithms, statistics, mathematics, machine learning, and programming languages such as Python, C, etc. They should also have an understanding of trade and the aptitude to frame the right questions to ask and find the answers from the available data. Finally, a data scientist should be able to communicate the outcomes efficiently to the team members and all the involved stakeholders.

Salary of a Data Analyst:

The salary for a Data Analyst may differ in different organizations. But, a Senior Data Analyst with the right skill and software knowledge may command a high price for the services offered.

The average salary for an entry-level Data Analyst may start from INR 2.9 lakhs per annum.

The average salary for a mid-level Data Analyst may start from INR 4.5 lakhs per annum.

The average salary for a Senior level Data Analyst may start from INR 9.5 lakhs per annum.

Some useful links are Below:

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[rohitbond]

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[python-coaching]

What is Python language and its Benefits ?

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[sayoutloud]

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[techgeecs]

Best Python training course in Chandigarh Mohali Panchkula

TechGeecs Inc. is the provider of the best Python training course in Chandigarh Mohali Panchkula. This Python Training course in Chandigarh’s Python programming language is a one-stop solution; also, Python Training Course in Chandigarh can be used for

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[ruhiparveen]

Data analytics for social media involves leveraging big data to enhance engagement strategies. By analyzing vast amounts of user-generated data, businesses can gain valuable insights into audience demographics, preferences, and behavior. This information helps optimize content and engagement strategies to increase user engagement and improve ROI. Data analytics also enables tracking of campaign performance in real-time, allowing for adjustments to be made to enhance effectiveness.

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[ridhisingla001]

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[techguides67]

Understanding the Four Types of Analytics and Their Practical Applications

In today's data-driven world, understanding different types of analytics is crucial for businesses to make informed decisions. Data analytics training course equips professionals with the skills needed to leverage these analytics effectively. Let's delve into the four primary types of analytics and explore how they are applied. Analytics refers to the systematic computational online data analysis course of or statistics. It involves discovering meaningful patterns, insights, and trends within datasets. Businesses use analytics to optimize processes, enhance decision-making, and gain competitive advantages.

Data analytics online training is crucial for mastering descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics summarizes historical data, diagnostic analytics identifies causes of events, predictive analytics forecasts future outcomes, and prescriptive analytics recommends actions. Businesses use these insights to enhance operational efficiency, improve customer engagement, and make informed decisions. Leveraging these analytics through training enables organizations to transform raw data into actionable strategies, gaining a competitive advantage in today's data-driven world.

The Four Types of Analytics

Descriptive Analytics

Descriptive analytics involves examining historical data to understand past events and outcomes. This type of analytics answers the question: "What has happened?" It summarizes and interprets data to provide insights into trends and patterns. For instance, a retail store may use descriptive analytics to review sales data from the previous quarter and identify which products were the top sellers.

Diagnostic Analytics

Diagnostic analytics focuses on analyzing data to determine why certain events occurred. It delves deeper into cause-and-effect relationships within the data. This type of analytics helps businesses understand the root causes of successes or failures. An example of diagnostic analytics is investigating customer churn by analyzing customer behavior and identifying common factors leading to attrition.

Predictive Analytics

Predictive analytics involves forecasting future outcomes based on historical data and statistical algorithms. It answers the question: "What is likely to happen?" This type of analytics is valuable for making proactive decisions and mitigating risks. For instance, an insurance company might use predictive analytics to assess the likelihood of claims based on customer demographics and historical claim data.

Prescriptive Analytics

Prescriptive analytics goes a step further by recommending actions to optimize outcomes. It combines insights from descriptive, diagnostic, and predictive analytics to provide actionable recommendations. This type of analytics helps businesses make data-driven decisions in real-time. For example, a logistics company can use prescriptive analytics to optimize delivery routes based on traffic patterns, weather forecasts, and customer preferences.

Applying Analytics in Business

Enhancing Operational Efficiency

Businesses can use best data analytics training to apply descriptive analytics to analyze operational data, identify inefficiencies, and streamline processes. Diagnostic analytics helps uncover the reasons behind bottlenecks or issues, enabling organizations to make targeted improvements.

Improving Customer Engagement

Predictive analytics is instrumental in understanding customer behavior and preferences. By analyzing past interactions and purchase history, businesses can predict future buying patterns and tailor marketing strategies accordingly to boost engagement and retention.

Risk Management and Decision-making

Prescriptive analytics aids in risk management by recommending optimal strategies to mitigate potential risks. For instance, financial institutions use prescriptive analytics to assess credit risk and determine suitable loan terms for applicants based on their financial profiles. In conclusion, data analytics course training is essential for mastering the four types of analytics: descriptive, diagnostic, predictive, and prescriptive. Each type plays a unique role in transforming raw data into actionable insights. By leveraging these analytics effectively, businesses can enhance operations, improve decision-making, and gain a competitive edge in today's data-driven landscape. Understanding and applying these analytics through proper training is key to harnessing the power of data for organizational success.

What is HR analytics?

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[newtecheducation]

What It is, How It's Used, and Four Fundamental Methods

In today's digital age, the vast amount of data generated presents both challenges and opportunities for organizations. Data analytics is a powerful tool that helps make sense of this data by analyzing it to uncover valuable insights. In this blog post, we'll explore what data analytics is, how it's used, and four basic techniques employed in data analytics. We'll also discuss the importance of data analytics training in mastering these techniques.

Understanding Data Analytics:

Data analyst course is the process of examining large datasets to uncover patterns, trends, and insights that can inform decision-making and drive business strategies. It involves collecting, cleaning, processing, and analyzing data to extract actionable insights. Data analytics can be applied in various domains, including finance, healthcare, marketing, and beyond.

How Data Analytics Is Used:

Business Intelligence: Data analytics helps organizations gain insights into their operations, customers, and market trends to make informed decisions and drive growth.

Healthcare: Data analytics is used to analyze patient data, predict disease outbreaks, and improve treatment outcomes.

Marketing: Data analytics enables marketers to target audiences more effectively, personalize marketing campaigns, and measure campaign performance.

Finance: Data analytics is used for risk assessment, fraud detection, and investment analysis, helping financial institutions make better decisions.

Basic Techniques in Data Analytics:

Descriptive Analytics: Descriptive analytics involves summarizing historical data to understand what happened in the past. It includes techniques such as data visualization, dashboards, and reports.

Diagnostic Analytics: Diagnostic analytics aims to understand why certain events occurred by identifying patterns and relationships in data. It includes techniques such as correlation analysis and root cause analysis.

Predictive Analytics: Predictive analytics involves forecasting future outcomes based on historical data and statistical models. It includes techniques such as regression analysis, time series forecasting, and machine learning algorithms.

Prescriptive Analytics: Prescriptive analytics goes beyond predicting future outcomes to recommend actions that can optimize results. It includes techniques such as optimization algorithms and decision trees.

Importance of Data Analytics Training:

Data analytics training is essential for mastering these techniques and effectively leveraging data to drive business outcomes. Training programs cover topics such as data manipulation, statistical analysis, machine learning, and data visualization, providing individuals with the skills and knowledge needed to excel in data analytics roles. By investing in data analytics training, organizations can ensure that their employees are equipped with the expertise needed to harness the power of data analytics and make informed decisions.

Data analytics is a powerful tool that helps organizations make sense of large datasets to uncover insights and drive business strategies. By understanding what data analytics is, how it's used, and mastering basic techniques, individuals and organizations can unlock the full potential of data analytics. Data analytics training plays a crucial role in equipping individuals with the skills and knowledge needed to succeed in this rapidly evolving field. With the right training and expertise, organizations can leverage data analytics to gain a competitive edge and achieve their business goals.

Certified data analyst:

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