Statistics For Management 2 Marks With Answers Pdf
Leto Corrales
In IBM's Data Analyst Career Guide and Interview Preparation course, you'll learn how to describe your role as a data analyst to others, explore the typical job interview cycle, and practice effective job interviewing techniques.
Outline the main tasks of a data analyst: identify, collect, clean, analyze, and interpret. Talk about how these tasks can lead to better business decisions, and be ready to explain the value of data-driven decision-making.
Many businesses have more data at their disposal than ever before. Hiring managers want to know you can work with large, complex data sets. Focus your answer on the size and type of data. How many entries and variables did you work with? What types of data were in the set?
The work of a data analyst involves a range of tasks and skills. Interviewers will likely ask questions specific to various parts of the data analysis process to evaluate how well you perform each step.
With this type of question (sometimes called a guesstimate), the interviewer presents you with a problem to solve. How would you estimate the best month to offer a discount on shoes? How would you estimate the weekly profit of your favorite restaurant?
Tip: In some cases, your interviewer might not be involved in data analysis. The entire interview, then, is an opportunity to demonstrate your ability to communicate clearly. Consider practicing your answers on a non-technical friend or family member.
Effective data analysts let the data tell the story. After all, data-driven decisions are based on facts rather than intuition or gut feelings. When asking this question, an interviewer might be trying to determine:
Interviewers will be looking for candidates who can leverage a wide range of technical data analyst skills. These questions are geared toward evaluating your competency across several skills. If you're preparing for an entry-level data analyst job and you don't have much experience yet, you might consider filling the gaps in your resume with a certificate. You can practice statistical analysis, data management, and programming using SQL, Tableau, and Python in Meta's beginner-friendly Data Analyst Professional Certificate. Designed to prepare you for an entry-level role, this self-paced program can be completed in just 5 months.
Similar to the last type of question, these interview questions help determine your knowledge of analytics concepts by asking you to compare two related terms. Some pairs you might want to be familiar with include:
Set yourself up for success in your next data analyst interview by using these questions alongside the Coursera Interview Guide. Get tips on formatting your answers using the STAR framework, researching the company, and tailoring your answers to the job.
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Data analytics is widely used in every sector in the 21st century. A career in the field of data analytics is highly lucrative in today's times, with its career potential increasing by the day. Out of the many job roles in this field, a data analyst's job role is widely popular globally. A data analyst collects and processes data; he/she analyzes large datasets to derive meaningful insights from raw data.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze the data. Thereafter it gets ready to be used with another dataset.
The answer to this question may vary from a case to case basis. However, some general strengths of a data analyst may include strong analytical skills, attention to detail, proficiency in data manipulation and visualization, and the ability to derive insights from complex datasets. Weaknesses could include limited domain knowledge, lack of experience with certain data analysis tools or techniques, or challenges in effectively communicating technical findings to non-technical stakeholders.
This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset.
Time Series analysis is a statistical procedure that deals with the ordered sequence of values of a variable at equally spaced time intervals. Time series data are collected at adjacent periods. So, there is a correlation between the observations. This feature distinguishes time-series data from cross-sectional data.
Ans: The choice of handling technique depends on factors such as the amount and nature of missing data, the underlying analysis, and the assumptions made. It's crucial to exercise caution and carefully consider the implications of the chosen approach to ensure the integrity and reliability of the data analysis. However, a few solutions could be:
Outlier detection is the process of identifying observations or data points that significantly deviate from the expected or normal behavior of a dataset. Outliers can be valuable sources of information or indications of anomalies, errors, or rare events.
It's important to note that outlier detection is not a definitive process, and the identified outliers should be further investigated to determine their validity and potential impact on the analysis or model. Outliers can be due to various reasons, including data entry errors, measurement errors, or genuinely anomalous observations, and each case requires careful consideration and interpretation.
LOD in Tableau stands for Level of Detail. It is an expression that is used to execute complex queries involving many dimensions at the data sourcing level. Using LOD expression, you can find duplicate values, synchronize chart axes and create bins on aggregated data.
Feature selection is the process of selecting a subset of relevant features from a larger set of variables or predictors in a dataset. It aims to improve model performance, reduce overfitting, enhance interpretability, and optimize computational efficiency. Here's an overview of the process and its importance:
- Improved Model Performance: By selecting the most relevant features, the model can focus on the most informative variables, leading to better predictive accuracy and generalization.
- Overfitting Prevention: Including irrelevant or redundant features can lead to overfitting, where the model learns noise or specific patterns in the training data that do not generalize well to new data. Feature selection mitigates this risk.
- Interpretability and Insights: A smaller set of selected features makes it easier to interpret and understand the model's results, facilitating insights and actionable conclusions.
- Computational Efficiency: Working with a reduced set of features can significantly improve computational efficiency, especially when dealing with large datasets.
Extract: Extract is an image of the data that will be extracted from the data source and placed into the Tableau repository. This image(snapshot) can be refreshed periodically, fully, or incrementally.
A Gantt chart in Tableau depicts the progress of value over the period, i.e., it shows the duration of events. It consists of bars along with the time axis. The Gantt chart is mostly used as a project management tool where each bar is a measure of a task in the project.
From the above map, it is clear that states like Washington, California, and New York have the highest sales and profits. While Texas, Pennsylvania, and Ohio have good amounts of sales but the least profits.
To create a DataFrame in Python, you need to import the Pandas library and use the read_csv function to load the .csv file. Give the right location where the file name and its extension follow the dataset.
Now that you know the different data analyst interview questions that can be asked in an interview, it is easier for you to crack for your coming interviews. Here, you looked at various data analyst interview questions based on the difficulty levels. And we hope this article on data analyst interview questions is useful to you.
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To prepare for a data analyst interview, review key concepts like statistics, data analysis methods, SQL, and Excel. Practice with real datasets and data visualization tools. Be ready to discuss your experiences and how you approach problem-solving. Stay updated on industry trends and emerging tools to demonstrate your enthusiasm for the role.
Data analyst interviews often include questions about handling missing data, challenges faced during previous projects, and data visualization tool proficiency. You might also be asked about analyzing A/B test results, creating data reports, and effectively collaborating with non-technical team members.
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