Suggested Certification for Big Data Analyst

Cloudera Certified Professional, Intellipaat Big Data Hadoop Certification, MCSE: Data Management and Analytics, MongoDB Certified Developer, EMC Data Science and Big Data Analytics Certification, Data Science Council of America Certification
Recommended Book 1 for Big Data Analyst

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Interview Questions and Answers

Data governance is a set of policies and procedures that ensure data quality, security, and compliance. It is crucial in Big Data to maintain the integrity and reliability of the data being used for analysis and decision-making.

Ethical considerations include ensuring data privacy, avoiding bias in algorithms, being transparent about data usage, and using data for beneficial purposes.

Common data cleaning techniques include handling missing values, removing duplicates, correcting errors, standardizing data formats, and filtering out irrelevant data.

Data security and privacy are ensured through various measures, including data encryption, access control, anonymization, and compliance with relevant regulations (e.g., GDPR, CCPA).

The success of a Big Data Analytics project can be measured by various metrics, including business impact, cost savings, increased efficiency, improved decision-making, and customer satisfaction.

Common challenges include data volume, velocity, variety, veracity (data quality), security, and the need for skilled data professionals.

You can learn Big Data Analytics through online courses, university programs, bootcamps, and self-study. Focus on developing skills in programming, statistics, data warehousing, and data visualization.

Structured data is organized in a predefined format (e.g., relational databases). Semi-structured data has some organizational properties but doesnt conform to a relational database (e.g., JSON, XML). Unstructured data doesnt have a predefined format (e.g., text, images, videos).

Missing data can be handled through various techniques, including deletion, imputation (replacing missing values with estimated values), or using algorithms that can handle missing data.

Big Data Analytics can be used for various business applications, including customer segmentation, targeted marketing, fraud detection, risk management, supply chain optimization, and predictive maintenance.

NoSQL databases are non-relational databases that are designed to handle large volumes of unstructured or semi-structured data. They are used in Big Data because they offer scalability, flexibility, and high performance compared to traditional relational databases.

Data Warehousing is the process of collecting and storing data from multiple sources into a central repository for reporting and analysis. It provides a structured and consolidated view of data for decision-making.

Data visualization helps to translate complex data into easily understandable charts, graphs, and dashboards. This allows analysts and stakeholders to quickly identify trends, patterns, and outliers, leading to better insights and decision-making.

Machine Learning algorithms can be used to automate data analysis tasks, identify patterns, and make predictions based on large datasets. This helps to uncover valuable insights and improve decision-making accuracy.

Key skills include proficiency in programming languages (Python, R, Java), data warehousing tools (SQL, Hadoop, Spark), statistical analysis, data visualization (Tableau, Power BI), and strong communication and problem-solving abilities.

Different roles include Data Scientist, Data Engineer, Big Data Architect, Business Intelligence Analyst, and Data Analyst, each with varying responsibilities and skill sets.

Hadoop is an open-source framework that allows for the distributed storage and processing of large datasets across clusters of computers. Its important because it enables the efficient handling and analysis of the massive volumes of data that characterize big data.

Spark is a fast, in-memory data processing engine that can work with Hadoop. While Hadoop uses MapReduce for batch processing, Spark offers real-time and near-real-time processing capabilities and is generally faster for iterative algorithms.

Popular tools include Hadoop, Spark, Hive, Pig, SQL, NoSQL databases (MongoDB, Cassandra), Tableau, Power BI, R, and Python.

Big Data Analytics is the process of examining large and varied data sets – i.e., big data – to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more informed business decisions.

The data analyst is someone who gathers, manages and conducts statistical data analysis.  This will translate number and data into a understandable language for organizations and businesses to make better business decisions.

Sisense, Looker, Qualtrics Research Core, Zoho Analytics, Reveal, Yellowfin, Periscope Data, Domo, Qlik Sense, GoodData, Birst, IBM Analytics, IBM Cognos, IBM Watson, MATLAB, Google Analytics, Apache Hadoop, Apache Spark, SAP Business Intelligence Platfo

Understand the business and frame the problem; collect the raw data, process data for analysis; Clean your data and enrich your dataset; Build helpful visualizations; Get predictive and communicate results of the analysis.

In probability theory, a normal distribution is a type of continuous probability distribution for a real-valued random variable. The general form of it's probability density function is The parameter is the mean or expectation of the distribution; and is

(1)Eigenvector and Eigenvalues - Eigenvectors make linear transformations understand easily. They are the axes along which a linear transformation acts simply by stretching/compressing and/or flipping; eigenvalues give you the factors by which this compre

The statistical power of an A/B test refers to the test sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (a) if a true effect of a certain magnitude (ME

What are the types of Hypothesis Testing?

A: Normality: tests for normal distribution in a population sample. T-test: tests for a Students t-distribution–ie, in a normally distributed population where standard deviation in unknown and sample s

 Take into considerations factors such as busiest months, big crowds, working hours etc..

Data cleansing or cleaning is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modif

The logistic model is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat,

 R Programming, Tableau Public, SAS, Apache Spark, Excel, RapidMiner, KNIME, QlikView.

Difference between Data Analysis-Data Mining: Also known as Knowledge discovery in databases-Data analysis can be divided into descriptive statistics,  exploratory data analysis, and confirmatory data analysis; It is the process of extracting important pa

 The importance of data in decision lies in consistency and continual growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights.

The missing data pattern is said to be monotone if the variables Yj can be ordered in such a way that if Yj is missing then all variables Yk with k > j are also missing. This occurs, for example, in longitudinal, drop-out studies. If the pattern is not mo

Data validation is a form of data cleansing used for checking the accuracy and quality of data, performed prior to importing and processing.  Data validation ensures that your data has no blank or null values, is unique, and the range of values is consist

Duplicates, incomplete data, inconsistent formats, accessibility, system upgrades, data purging and storage.

 SQL, Excel, critical thinking, R or Python–Statistical programming, data visualization, presentation skills, machine learning.

Mean, standard deviation, regression, sample size determination, hypothesis testing.

 Explain with examples and numbers

 Explain with examples and convince there were no conflicts

Python, SQL and R are the most popular programming languages.