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• COLLECTING MEANINGFUL and real-time DATA.
• Visual representation of data.
• Data from multiple sources.
• Inaccessible data.
• Poor quality data.

• Gather data.
• Discover and assess data.
• Cleanse and validate data.
• Transform and enrich data.
• Store data.

Collaborative filtering MAY be a technique that will FILTER items that a user might like BASED on the IDEA of reactions by similar users. It works by searching an outsized group of individuals and finding a smaller set of users with tastes almost like a specific user.

• CHECK digit.
• Format check.
• Length check.
• Lookup table.
• Presence check.
• Range check.
• Spell check.

K-intends to ONE of the most natural individual LEARNING calculations that help in taking care of the acclaimed bunching issue. The system follows a straightforward and straightforward approach to group a given informational index through a specific number of bunches (accept k bunches) fixed earlier. The principle thought is to CHARACTERIZE k focuses, one for each group.

The SEVEN CHARACTERISTICS that DEFINE a good data MODEL are:

• Accuracy and Precision.
• Legitimacy and Validity.
• Reliability and Consistency.
• Timeliness and Relevance.
• Completeness and Comprehensiveness.
• Availability and Accessibility.
• Granularity and UNIQUENESS.

Note: This is one of those data analyst interview questions which is often asked in the interview

• BUSINESS issue understating.
• Understanding your data set.
• Data Preparation.
• Exploratory Analysis/ Modelling.
• Validation.
• Visualization & presentation.

Data cleaning - is the process of recognizing inaccurate or unethical data from a database. To ensure that the customer data is employed within the most efficient and meaningful manner , which will INCREASE the elemental value of the brand, business ENTERPRISES must give importance to data quality.

• For enormous datasets, break them into little information. Working with less information will SPEED up.
• If you've got a problem with data cleanliness, arrange them by estimated frequency and attack the foremost common problems
• Break down the synopsis measurements for every section (standard DEVIATION, mean, number of missing qualities).
• Keep track of each DATE cleaning operation, so you'll alter changes or remove activities if required.

• RECOGNIZE comparative information RECORDS and CONSOLIDATE them into one record that will contain all the valuable properties.
• Encourage pattern RECONCILIATION through CONSTRUCTION rebuilding.

UNIVARIATE

• Univariate analysis is the investigation of one ("uni") variable.
• The primary purpose of the univariate analysis is to explain the INFO and FIND patterns that exist within it.

• The bivariate investigation is probably the least complicated type of quantitative examination. It includes the study of two factors, to decide the observational connection between them
• The bivariate examination can be useful in testing straightforward THEORIES of affiliation.

• Multivariate Analysis is the investigation of three or more variables.
• There are some ways to perform STATISTICAL methods, depending on your goals. Methods like - Additive Tree, Canonical CORRELATION Analysis, Cluster Analysis, Correspondence Analysis / Multiple Correspondence Analysis, correlational Analysis, Generalized Procrustes Analysis

• R Programming
• Tableau PUBLIC:
• Python
• SAS
• Apache SPARK

• PARTITIONING methods.
• Hierarchical CLUSTERING.
• Fuzzy clustering.
• Density-based clustering.
• Model-based clustering.

• Z-Score or EXTREME VALUE Analysis (parametric).
• Probabilistic and STATISTICAL Modelling (parametric).