Data Preparation for Analytics Projects PDF

20XX How should data preparation be done for an analytics project? David Manne [email protected] Content 01 Introduction to Data Preparation s 02 Data Collection 03 Data Cleaning 04 Data Transformation 05 Data...

20XX How should data preparation be done for an analytics project? David Manne [email protected] Content 01 Introduction to Data Preparation s 02 Data Collection 03 Data Cleaning 04 Data Transformation 05 Data Reduction 06 Data Integration 07 Data Validation and Verification  01 Introduction to Data Preparation Importance of Data Preparation Enhancing Data Quality Removing errors and inconsistencies Ensuring data accuracy Standardizing data formats Impact on Analysis Results Improving predictive model performance Ensuring reliable insights Reducing biases in results Time and Resource Efficiency Reducing manual rework Streamlining data processing workflows Facilitating faster data analysis Overview of Data Preparation Process Data Collection Data Cleaning Data Transformation Identifying data sources Removing duplicates Normalizing data Gathering raw data Correcting errors formats Assessing data Standardizing data Aggregating data points Creating new relevance values calculated fields 01 02 03 Common Challenges in Data Preparation Handling Missing Data Managing Large Datasets Identifying missing values Dealing with Inconsistent Utilizing efficient storage solutions Imputing missing data Implementing data sampling Data techniques Deciding on exclusion criteria Leveraging distributed computing Detecting inconsistent entries systems Harmonizing data variations Implementing data validation rules  02 Data Collection Identifying Data Sources Part 01 Part 02 Part 03 Internal Data Sources External Data Sources Public Data Repositories Departmental databases Third- party vendors Government databases Company intranets Market research reports Open- source datasets Employee- generated Customer feedback Academic research data from external databases platforms Methods of Data Collection Surveys and Questionnaires 0 Online survey platforms (e.g., SurveyMonkey) Paper- based questionnaires 1 Mobile app surveys Automated Data Collection 02 Sensor data collection Internet of Things (IoT) devices Software application logs Data Scraping 03 Web scraping tools (e.g., Beautiful Soup) Automated bots for data extraction Custom scripting for web data collection Tools for Data Collection 01 02 03 Database Management Systems APIs and Web Services Data Integration Platforms SQL- based systems (e.g., MySQL, RESTful APIs ETL tools (e.g., Talend, Apache PostgreSQL) SOAP- based web services Nifi) NoSQL databases (e.g., MongoDB, Data warehousing solutions Public API integrators (e.g., Cassandra) (e.g., Snowflake) Zapier) Cloud- based solutions (e.g., Data lake platforms (e.g., Google BigQuery) AWS Lake Formation)  03 Data Cleaning Handling Missing Values Identifying Missing Data Imputation Techniques Handling Entire Missing Records Methods to detect missing datanull Mean/Median/Mode imputation for Removing records with substantial checks, summary statistics numerical data missing data Visualizing missing data with Using regression models for more Evaluating the impact of removing heatmaps accurate imputation records on the dataset Differentiating between data missing Employing k- Nearest Neighbors Best practices for documenting at random and not at random algorithm for imputation removed records Correcting Inaccurate Data 01 02 03 Data Validation Techniques Regular Expression Usage Standardizing Data Formats Implementing data type checks Validating email addresses and Converting data to consistent and constraints phone numbers formats (dates, strings) Cross- referencing with external Cleaning text dataremoving Defining and applying formatting datasets for verification unwanted characters, spaces standards across the dataset Using checksum algorithms Regular expressions for Automation tools for for integrity verification detecting patterns in data standardizing large datasets Dealing with Outliers Detecting Outliers Outlier Treatment Techniques Impact of Outliers on Analysis Statistical methodsZ- score, IQR Transforming data to reduce impact Potential distortion of statistical method (log transformation) summaries and models Visualization tools: box plots, scatter Winsorizing data to limit extreme Understanding and addressing plots values biases introduced by outliers Software tools and libraries for Using robust statistical methods Strategies for appropriately outlier detection less sensitive to outliers reporting and documenting outliers  04 Data Transformation Data Normalization Importance of Techniques for Tools for Normalization Normalization Normalization Ensures data conformity for Min- Max Scaling Scikit- Learn machine learning applications Z- Score Standardization pandas Improves model accuracy and Log Transformation NumPy training process efficiency Reduces redundancy and variability in the dataset Data Encoding Categorical Data Encoding One- Hot Encoding Label Encoding Ordinal Encoding Feature Scaling Standard Scaler Min- Max Scaler Robust Scaler Encoding Text and Time Data Bag- of- Words Model TF- IDF Vectorization Time Series Encoding Techniques Data Aggregation Aggregation Use Cases for Tools to Aid Methods Aggregation Aggregation Sum Summarizing large datasets GroupBy in pandas Average (Mean) Building dashboards and SQL Aggregation Functions Median reports Apache Hadoop and Count Enhancing data Spark granularity for analysis  05 Data Reduction Data Sampling Methods Random Sampling Definition and basic concept of random sampling How to perform random samplingsimple random sampling vs. systematic random sampling Advantages and challenges of random sampling in data analysis Applications of random sampling in survey research and machine learning model training Stratified Sampling Understanding stratified sampling and when to use it Steps involved in conducting stratified samplingdividing the population into strata and sampling within each stratum Benefits of stratified sampling in improving representativeness and accuracy Examples of stratified sampling in real- world studies Systematic Sampling Introduction to systematic sampling and how it works Methodologyselecting a random starting point and picking every nth element Advantages and disadvantages of systematic sampling Use cases of systematic sampling in quality control and market research Dealing with Imbalanced Datasets Use of Synthetic Data Over-sampling Techniques Under-sampling Techniques Generation Explanation of over- sampling and its Definition of under- sampling and The concept of synthetic data generation need in handling imbalanced datasets common methods used and its role in balancing datasets Different over- sampling Techniques for under- samplingrandom Methods for generating synthetic techniquesSMOTE (Synthetic Minority under- sampling, Tomek links, and Cluster dataGANs (Generative Adversarial Over- sampling Technique), ADASYN Centroids Networks), data augmentation (Adaptive Synthetic Sampling) Benefits and drawbacks of under- Advantages of using synthetic data: Pros and cons of over- sampling methods sampling enhancing diversity, reducing bias Impact of over- sampling on model Considerations for applying under- Challenges of synthetic data performance and training time sampling to prevent data loss and generation: preserving data privacy, maintain model efficacy maintaining data integrity  06 Data Integration Combining Data from Multiple Sources Data Joining Handling Redundancies Data Merging Implementing SQL join Identifying duplicate operations records across datasets Leveraging NoSQL databases for Integrating datasets with similar structures De- duplication techniques flexible joins Strategies for combining and tools Consolidating data from Implementing master data relational and non- relational data different databases management protocols Using algorithms to blend datasets efficiently Ensuring Data Consistency 01 03 Data Reconciliation Techniques Addressing Data Conflicts 02 Automated Consistency Checks Conflict resolution strategies in Cross- referencing data entries data integration for accuracy Implementing version control Utilizing automated Implementing data validation systems reconciliation software rules Consistency algorithms for Manual reconciliation for Real- time data monitoring conflict resolution complex data anomalies systems Use of scripts and software for automated checks Metadata Management 01 02 03 Importance of Metadata Metadata Tools and Techniques Metadata Standards Understanding metadata's role in Metadata management software Commonly used metadata data integration solutions standards (e.g., Dublin Core, ISO Enhancing data discoverability Techniques for capturing and 19115) with metadata cataloging metadata Implementing standardized Supporting data governance and Workflow automation for metadata protocols compliance metadata updates Benefits of adhering to metadata standards  07 Data Validation and Verification Data Quality Assessment Data Accuracy Data Completeness Data Consistency Verification against original sources Ensuring all required fields are filled Standardizing data formats Cross- referencing with reputable Handling missing data Synchronizing data across systems data appropriately Regularly reconciling data entries Regular updates and corrections Tracking data entry processes Validation Techniques Manual Review Automated Validation Statistical Methods Cross- checking data entry Implementing validation rules Using statistical tools to identify Reviewing reports for anomalies in software outliers Double- checking critical data Utilizing data validation scripts Applying predictive models for points Automated error detection and validation correction Trend analysis to flag discrepancies Ensuring Data Integrity 01 02 03 15 Integrity Constraints Auditing and Monitoring Error Reporting Mechanisms Using primary and foreign Maintaining audit trails Automated error alerts keys Regular system audits and User feedback and Enforcing data type reviews reporting systems restrictions Monitoring access and Regular error logs and Referential integrity rules changes to data reviews 20XX Thanks Edited by David Raju 20XX-01-01 PPT DESIGN