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Data Mining.

Published by Sheryl Andrews Modified over 8 years ago

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Presentation on theme: "Data Mining."— Presentation transcript:

Overview of Data Mining & The Knowledge Discovery Process Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.

modified by Marius Bulacu

1 Introduction and Review CS 636 – Adv. Data Mining.

Data Mining: Concepts and Techniques

Data Mining Knowledge Discovery in Databases Data 31.

Dr. Tahar Kechadi Dr. Joe Carthy

Data Mining By Archana Ketkar.

Data Mining – Intro.

Data mining By Aung Oo.

Advanced Database Applications Database Indexing and Data Mining CS591-G1 -- Fall 2001 George Kollios Boston University.

CIT 858: Data Mining and Data Warehousing Course Instructor: Bajuna Salehe Web:

Data Mining: Concepts & Techniques. Motivation: Necessity is the Mother of Invention Data explosion problem –Automated data collection tools and mature.

OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.

Data Mining : Introduction Chapter 1. 2 Index 1. What is Data Mining? 2. Data Mining Functionalities 1. Characterization and Discrimination 2. MIning.

Data Warehouse Fundamentals Rabie A. Ramadan, PhD 2.

CS-470: Data Mining Fall Organizational Details Class Meeting: 4:00-6:45pm, Tuesday, Room SCIT215 Instructor: Dr. Igor Aizenberg Office: Science.

Data Mining Chapter 26.

10 Data Mining. What is Data Mining? “Data Mining is the process of selecting, exploring and modeling large amounts of data to uncover previously unknown.

Shilpa Seth.  What is Data Mining What is Data Mining  Applications of Data Mining Applications of Data Mining  KDD Process KDD Process  Architecture.

Chapter 1. Introduction Motivation: Why data mining?

About project

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Data Mining PowerPoint Templates & Presentation Slides

SlideModel brings you Data Mining PowerPoint Presentation templates that you can use in your projects related to data mining. From the collection to exploration, modeling, visualizing, simulating, and concluding the analysis, you’ll find stunning presentation templates here. In this section, you can find Data Mining slides and Data Mining PowerPoint presentation templates.

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Data mining refers to the process of identifying patterns and discovering knowledge from large data sets. It involves various methods, including statistics, machine learning, as well as database systems.

The main goal of data mining is to extract information from a large data set and to utilize that information into creating a comprehensive structure that can be used in further processes. It is the ‘analysis’ part of KDD (knowledge discovery in databases). Data mining can also involve various data management and database aspects, such as pre-processing of data, modeling, inference of data, considerations of complexity, post-processing, visualization, as well as online updating.

Data mining can be used to get accurate predictions of various results, which can then be reviewed by a decision support system of a business or organization.

What is Data Mining?

Data Mining is a process of extracting useful information from a large data set. The process involves collection, analysis, processing, visualization, simulation, and conclusion of data. The word ‘data mining’ can be a little misleading because it does not mean the actual extraction of data itself, but rather refers to extracting useful information and results from it.

What is a Data Mining PowerPoint template?

A Data Mining PowerPoint template is a presentation template that presenters can use to demonstrate the process of data mining and for showcasing the results to the respective stakeholders. These templates include various charts, graphs, illustrations, and text placeholders that can be personalized by downloading and editing the slides on PowerPoint, Google Slides, or Keynote.

How can a Data Mining PowerPoint template be used in a presentation?

You can use a Data Mining PowerPoint template in your presentation by first customizing it according to your requirements. SlideModel offers hundred-percent downloadable and editable presentation templates. You can simply download the template on your PC or laptop and edit it on PowerPoint, Google Slides, or Keynote. You can change the colors, pointers, text, and size of the template as per your need.

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Our annual unlimited plan let you download unlimited content from slidemodel. save hours of manual work and use awesome slide designs in your next presentation..

Jiawei Han, Micheline Kamber and Jian Pei

Data Mining: Concepts and Techniques, 3 rd ed .

The Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791

Slides in PowerPoint

Chapter 1. Introduction

Chapter 2. Know Your Data

Chapter 3. Data Preprocessing

Chapter 4. Data Warehousing and On-Line Analytical Processing

Chapter 5. Data Cube Technology

Chapter 6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods

Chapter 7. Advanced Frequent Pattern Mining

Chapter 8. Classification: Basic Concepts

Chapter 9. Classification: Advanced Methods

Chapter 10. Cluster Analysis: Basic Concepts and Methods

Chapter 11. Cluster Analysis: Advanced Methods

Chapter 12. Outlier Detection

Chapter 13. Trends and Research Frontiers in Data Mining

Updated Slides for CS, UIUC Teaching in PowerPoint form

(Note: This set of slides corresponds to the current teaching of the data mining course at CS, UIUC.  In general, it takes new technical materials from recent research papers but shrinks some materials of the textbook.  It has also re-arranged the order of presentation for some technical materials.)

Instructions on finding the new sets of slides are as follows:

1.        Go to the homepage of the first author, Prof.  Jiawei  Han:  http://web.engr.illinois.edu/~hanj/

2.        Click the following links in the section of Teaching:

a .        UIUC CS412: An Introduction to Data Warehousing and Data Mining 

b .        UIUC CS512: Data Mining: Principles and Algorithms

3.        Download the slides of the corresponding chapters you are interested in

Back to Data Mining: Concepts and Techniques, 3 rd ed .

Back to jiawei han , data and information systems research laboratory , computer science, university of illinois at urbana-champaign.

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Deliver this complete deck to your team members and other collaborators. Encompassed with stylized slides presenting various concepts, this Data Mining Powerpoint Presentation Slides is the best tool you can utilize. Personalize its content and graphics to make it unique and thought provoking. All the eighty two slides are editable and modifiable, so feel free to adjust them to your business setting. The font, color, and other components also come in an editable format making this PPT design the best choice for your next presentation. So, download now.

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Data Mining: Association

Nov 01, 2011

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Data Mining: Association. Mining Association Rules in Large Databases. Association rule mining Mining single-dimensional Boolean association rules from transactional databases Mining multilevel association rules from transactional databases

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• reduced support
• lower support threshold
• association mining
• subsequent scans

Presentation Transcript

Mining Association Rules in Large Databases • Association rule mining • Mining single-dimensional Boolean association rules from transactional databases • Mining multilevel association rules from transactional databases • Mining multidimensional association rules from transactional databases and data warehouse • From association mining to correlation analysis • Constraint-based association mining • Summary

What Is Association Mining? • Association rule mining: • Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. • Applications: • Basket data analysis, cross-marketing, catalog design, loss-leader analysis, clustering, classification, etc. • Examples. • Rule form: “Body ® Head [support, confidence]”. • buys(x, “diapers”) ® buys(x, “beers”) [0.5%, 60%] • major(x, “CS”) ^ takes(x, “DB”) ® grade(x, “A”) [1%, 75%]

Association Rule: Basic Concepts • Given: (1) database of transactions, (2) each transaction is a list of items (purchased by a customer in a visit) • Find: all rules that correlate the presence of one set of items with that of another set of items • E.g., 98% of people who purchase tires and auto accessories also get automotive services done • Applications • Maintenance Agreement (What the store should do to boost Maintenance Agreement sales) • Home Electronics (What other products should the store stocks up?) • Attached mailing in direct marketing • Detecting “ping-pong”ing of patients, faulty “collisions”

Rule Measures: Support and Confidence Customer buys both Customer buys diaper • Find all the rules X & Y  Z with minimum confidence and support • support, s, probability that a transaction contains {X  Y  Z} • confidence, c, conditional probability that a transaction having {X  Y} also contains Z Customer buys beer • Let minimum support 50%, and minimum confidence 50%, we have • A  C (50%, 66.6%) • C  A (50%, 100%)

Association Rule Mining: A Road Map • Boolean vs. quantitative associations (Based on the types of values handled) • buys(x, “SQLServer”) ^ buys(x, “DMBook”) ® buys(x, “DBMiner”) [0.2%, 60%] • age(x, “30..39”) ^ income(x, “42..48K”) ® buys(x, “PC”) [1%, 75%] • Single dimension vs. multiple dimensional associations • Single level vs. multiple-level analysis • What brands of beers are associated with what brands of diapers?

Mining Association Rules—An Example Min. support 50% Min. confidence 50% For rule AC: support = support({AC}) = 50% confidence = support({AC})/support({A}) = 66.6% The Apriori principle: Any subset of a frequent itemset must be frequent

Mining Frequent Itemsets: the Key Step • Find the frequent itemsets: the sets of items that have minimum support • A subset of a frequent itemset must also be a frequent itemset • i.e., if {AB} isa frequent itemset, both {A} and {B} should be a frequent itemset • Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset) • Use the frequent itemsets to generate association rules.

The Apriori Algorithm • Join Step: Ckis generated by joining Lk-1with itself • Prune Step: Any (k-1)-itemset that is not frequent cannot be a subset of a frequent k-itemset • Pseudo-code: Ck: Candidate itemset of size k Lk : frequent itemset of size k L1 = {frequent items}; for(k = 1; Lk !=; k++) do begin Ck+1 = candidates generated from Lk; for each transaction t in database do increment the count of all candidates in Ck+1 that are contained in t Lk+1 = candidates in Ck+1 with min_support end returnkLk;

The Apriori Algorithm — Example Database D L1 C1 Scan D C2 C2 L2 Scan D L3 C3 Scan D

How to Generate Candidates? • Suppose the items in Lk-1 are listed in an order • Step 1: self-joining Lk-1 insert intoCk select p.item1, p.item2, …, p.itemk-1, q.itemk-1 from Lk-1 p, Lk-1 q where p.item1=q.item1, …, p.itemk-2=q.itemk-2, p.itemk-1 < q.itemk-1 • Step 2: pruning forall itemsets c in Ckdo forall (k-1)-subsets s of c do if (s is not in Lk-1) then delete c from Ck

How to Count Supports of Candidates? • Why counting supports of candidates a problem? • The total number of candidates can be very huge • One transaction may contain many candidates • Method: • Candidate itemsets are stored in a hash-tree • Leaf node of hash-tree contains a list of itemsets and counts • Interior node contains a hash table • Subset function: finds all the candidates contained in a transaction

Example of Generating Candidates • L3={abc, abd, acd, ace, bcd} • Self-joining: L3*L3 • abcd from abc and abd • acde from acd and ace • Pruning: • acde is removed because ade is not in L3 • C4={abcd}

Methods to Improve Apriori’s Efficiency • Hash-based itemset counting: A k-itemset whose corresponding hashing bucket count is below the threshold cannot be frequent • Transaction reduction: A transaction that does not contain any frequent k-itemset is useless in subsequent scans • Partitioning: Any itemset that is potentially frequent in DB must be frequent in at least one of the partitions of DB • Sampling: mining on a subset of given data, lower support threshold + a method to determine the completeness • Dynamic itemset counting: add new candidate itemsets only when all of their subsets are estimated to be frequent

Visualization of Association Rule Using Plane Graph

Food bread milk 2% white wheat skim Fraser Sunset Multiple-Level Association Rules • Items often form hierarchy. • Items at the lower level are expected to have lower support. • Rules regarding itemsets at appropriate levels could be quite useful. • Transaction database can be encoded based on dimensions and levels • We can explore shared multi-level mining

Mining Multi-Level Associations • A top_down, progressive deepening approach: • First find high-level strong rules: milk ® bread [20%, 60%]. • Then find their lower-level “weaker” rules: 2% milk ® wheat bread [6%, 50%]. • Variations at mining multiple-level association rules. • Level-crossed association rules: 2% milk ®Wonderwheat bread • Association rules with multiple, alternative hierarchies: 2% milk ®Wonder bread

Multi-level Association: Uniform Support vs. Reduced Support • Uniform Support: the same minimum support for all levels • + One minimum support threshold. No need to examine itemsets containing any item whose ancestors do not have minimum support. • – Lower level items do not occur as frequently. If support threshold • too high  miss low level associations • too low  generate too many high level associations • Reduced Support: reduced minimum support at lower levels • There are 4 search strategies: • Level-by-level independent • Level-cross filtering by k-itemset • Level-cross filtering by single item • Controlled level-cross filtering by single item

Uniform Support Multi-level mining with uniform support Level 1 min_sup = 5% Milk [support = 10%] 2% Milk [support = 6%] Skim Milk [support = 4%] Level 2 min_sup = 5% Back

Reduced Support Multi-level mining with reduced support Level 1 min_sup = 5% Milk [support = 10%] 2% Milk [support = 6%] Skim Milk [support = 4%] Level 2 min_sup = 3% Back

Multi-level Association: Redundancy Filtering • Some rules may be redundant due to “ancestor” relationships between items. • Example • milk  wheat bread [support = 8%, confidence = 70%] • 2% milk  wheat bread [support = 2%, confidence = 72%] • We say the first rule is an ancestor of the second rule. • A rule is redundant if its support is close to the “expected” value, based on the rule’s ancestor.

Multi-Level Mining: Progressive Deepening • A top-down, progressive deepening approach: • First mine high-level frequent items: milk (15%), bread (10%) • Then mine their lower-level “weaker” frequent itemsets: 2% milk (5%), wheat bread (4%) • Different min_support threshold across multi-levels lead to different algorithms: • If adopting the same min_support across multi-levels then toss t if any of t’s ancestors is infrequent. • If adopting reduced min_support at lower levels then examine only those descendents whose ancestor’s support is frequent/non-negligible.

Progressive Refinement of Data Mining Quality • Why progressive refinement? • Mining operator can be expensive or cheap, fine or rough • Trade speed with quality: step-by-step refinement. • Superset coverage property: • Preserve all the positive answers—allow a positive false test but not a false negative test. • Two- or multi-step mining: • First apply rough/cheap operator (superset coverage) • Then apply expensive algorithm on a substantially reduced candidate set (Koperski & Han, SSD’95).

Multi-Dimensional Association: Concepts • Single-dimensional rules: buys(X, “milk”)  buys(X, “bread”) • Multi-dimensional rules:  2 dimensions or predicates • Inter-dimension association rules (no repeated predicates) age(X,”19-25”)  occupation(X,“student”)  buys(X,“coke”) • hybrid-dimension association rules (repeated predicates) age(X,”19-25”)  buys(X, “popcorn”)  buys(X, “coke”) • Categorical Attributes • finite number of possible values, no ordering among values • Quantitative Attributes • numeric, implicit ordering among values

Interestingness Measurements • Objective measures Two popular measurements: • support; and • confidence • Subjective measures (Silberschatz & Tuzhilin, KDD95) A rule (pattern) is interesting if • it is unexpected (surprising to the user); and/or • actionable (the user can do something with it)

Criticism to Support and Confidence • Example 1: (Aggarwal & Yu, PODS98) • Among 5000 students • 3000 play basketball • 3750 eat cereal • 2000 both play basket ball and eat cereal • play basketball eat cereal [40%, 66.7%] is misleading because the overall percentage of students eating cereal is 75% which is higher than 66.7%. • play basketball not eat cereal [20%, 33.3%] is far more accurate, although with lower support and confidence

Criticism to Support and Confidence (Cont.) • Example 2: • X and Y: positively correlated, • X and Z, negatively related • support and confidence of X=>Z dominates • We need a measure of dependent or correlated events • P(B|A)/P(B) is also called the lift of rule A => B

Other Interestingness Measures: Interest • Interest (correlation, lift) • taking both P(A) and P(B) in consideration • P(A^B)=P(B)*P(A), if A and B are independent events • A and B negatively correlated, if the value is less than 1; otherwise A and B positively correlated

Constraint-Based Mining • Interactive, exploratory mining giga-bytes of data? • Could it be real? — Making good use of constraints! • What kinds of constraints can be used in mining? • Knowledge type constraint: classification, association, etc. • Data constraint: SQL-like queries • Find product pairs sold together in Vancouver in Dec.’98. • Dimension/level constraints: • in relevance to region, price, brand, customer category. • Rule constraints • small sales (price < $10) triggers big sales (sum > $200). • Interestingness constraints: • strong rules (min_support  3%, min_confidence  60%).

Summary • Association rule mining • probably the most significant contribution from the database community in KDD • A large number of papers have been published • Many interesting issues have been explored • An interesting research direction • Association analysis in other types of data: spatial data, multimedia data, time series data, etc.

References • R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent itemsets. In Journal of Parallel and Distributed Computing (Special Issue on High Performance Data Mining), 2000. • R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. SIGMOD'93, 207-216, Washington, D.C. • R. Agrawal and R. Srikant. Fast algorithms for mining association rules. VLDB'94 487-499, Santiago, Chile. • R. Agrawal and R. Srikant. Mining sequential patterns. ICDE'95, 3-14, Taipei, Taiwan. • R. J. Bayardo. Efficiently mining long patterns from databases. SIGMOD'98, 85-93, Seattle, Washington. • S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generalizing association rules to correlations. SIGMOD'97, 265-276, Tucson, Arizona. • S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket analysis. SIGMOD'97, 255-264, Tucson, Arizona, May 1997. • K. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg cubes. SIGMOD'99, 359-370, Philadelphia, PA, June 1999. • D.W. Cheung, J. Han, V. Ng, and C.Y. Wong. Maintenance of discovered association rules in large databases: An incremental updating technique. ICDE'96, 106-114, New Orleans, LA. • M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, and J. D. Ullman. Computing iceberg queries efficiently. VLDB'98, 299-310, New York, NY, Aug. 1998.

References (2) • G. Grahne, L. Lakshmanan, and X. Wang. Efficient mining of constrained correlated sets. ICDE'00, 512-521, San Diego, CA, Feb. 2000. • Y. Fu and J. Han. Meta-rule-guided mining of association rules in relational databases. KDOOD'95, 39-46, Singapore, Dec. 1995. • T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. SIGMOD'96, 13-23, Montreal, Canada. • E.-H. Han, G. Karypis, and V. Kumar. Scalable parallel data mining for association rules. SIGMOD'97, 277-288, Tucson, Arizona. • J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. ICDE'99, Sydney, Australia. • J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. VLDB'95, 420-431, Zurich, Switzerland. • J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. SIGMOD'00, 1-12, Dallas, TX, May 2000. • T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of ACM, 39:58-64, 1996. • M. Kamber, J. Han, and J. Y. Chiang. Metarule-guided mining of multi-dimensional association rules using data cubes. KDD'97, 207-210, Newport Beach, California. • M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo. Finding interesting rules from large sets of discovered association rules. CIKM'94, 401-408, Gaithersburg, Maryland.

References (3) • F. Korn, A. Labrinidis, Y. Kotidis, and C. Faloutsos. Ratio rules: A new paradigm for fast, quantifiable data mining. VLDB'98, 582-593, New York, NY. • B. Lent, A. Swami, and J. Widom. Clustering association rules. ICDE'97, 220-231, Birmingham, England. • H. Lu, J. Han, and L. Feng. Stock movement and n-dimensional inter-transaction association rules. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'98), 12:1-12:7, Seattle, Washington. • H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association rules. KDD'94, 181-192, Seattle, WA, July 1994. • H. Mannila, H Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1:259-289, 1997. • R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. VLDB'96, 122-133, Bombay, India. • R.J. Miller and Y. Yang. Association rules over interval data. SIGMOD'97, 452-461, Tucson, Arizona. • R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. SIGMOD'98, 13-24, Seattle, Washington. • N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. ICDT'99, 398-416, Jerusalem, Israel, Jan. 1999.

References (4) • J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. SIGMOD'95, 175-186, San Jose, CA, May 1995. • J. Pei, J. Han, and R. Mao. CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets. DMKD'00, Dallas, TX, 11-20, May 2000. • J. Pei and J. Han. Can We Push More Constraints into Frequent Pattern Mining? KDD'00. Boston, MA. Aug. 2000. • G. Piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases, 229-238. AAAI/MIT Press, 1991. • B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. ICDE'98, 412-421, Orlando, FL. • J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. SIGMOD'95, 175-186, San Jose, CA. • S. Ramaswamy, S. Mahajan, and A. Silberschatz. On the discovery of interesting patterns in association rules. VLDB'98, 368-379, New York, NY.. • S. Sarawagi, S. Thomas, and R. Agrawal. Integrating association rule mining with relational database systems: Alternatives and implications. SIGMOD'98, 343-354, Seattle, WA. • A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. VLDB'95, 432-443, Zurich, Switzerland. • A. Savasere, E. Omiecinski, and S. Navathe. Mining for strong negative associations in a large database of customer transactions. ICDE'98, 494-502, Orlando, FL, Feb. 1998.

References (5) • C. Silverstein, S. Brin, R. Motwani, and J. Ullman. Scalable techniques for mining causal structures. VLDB'98, 594-605, New York, NY. • R. Srikant and R. Agrawal. Mining generalized association rules. VLDB'95, 407-419, Zurich, Switzerland, Sept. 1995. • R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. SIGMOD'96, 1-12, Montreal, Canada. • R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. KDD'97, 67-73, Newport Beach, California. • H. Toivonen. Sampling large databases for association rules. VLDB'96, 134-145, Bombay, India, Sept. 1996. • D. Tsur, J. D. Ullman, S. Abitboul, C. Clifton, R. Motwani, and S. Nestorov. Query flocks: A generalization of association-rule mining. SIGMOD'98, 1-12, Seattle, Washington. • K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Computing optimized rectilinear regions for association rules. KDD'97, 96-103, Newport Beach, CA, Aug. 1997. • M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. Parallel algorithm for discovery of association rules. Data Mining and Knowledge Discovery, 1:343-374, 1997. • M. Zaki. Generating Non-Redundant Association Rules. KDD'00. Boston, MA. Aug. 2000. • O. R. Zaiane, J. Han, and H. Zhu. Mining Recurrent Items in Multimedia with Progressive Resolution Refinement. ICDE'00, 461-470, San Diego, CA, Feb. 2000.

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Explore and analyse large amounts of information thanks to these Big Data infographics. Create new commercial services, use them for marketing purposes or for research, no matter the topic. We have added charts, reports, gears, pie charts, text blocks, circle and cycle diagrams, pyramids and banners in different styles, such...

Data Analysis for Business

What helps employees of a company know how the business is performing and recognize current problems that are to be solved? Data analysis laid out in a presentation, for example. Since we all want to do our best in our jobs, this template can come in handy for you. Its...

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Math Subject for High School - 9th Grade: Data Analysis

Analyzing data is very helpful for middle schoolers! They will get it at the very first lesson if you use this template in your maths class. Visual representations of data, like graphs, are very helpful to understand statistics, deviation, trends… and, since math has many variables, so does our design:...

Software Development Through AI Pitch Deck

Download the "Software Development Through AI Pitch Deck" presentation for PowerPoint or Google Slides. Whether you're an entrepreneur looking for funding or a sales professional trying to close a deal, a great pitch deck can be the difference-maker that sets you apart from the competition. Let your talent shine out...

Statistics and Data Analysis - 6th Grade

Download the "Statistics and Data Analysis - 6th Grade" presentation for PowerPoint or Google Slides. If you’re looking for a way to motivate and engage students who are undergoing significant physical, social, and emotional development, then you can’t go wrong with an educational template designed for Middle School by Slidesgo!...

Data Analytics Strategy Toolkit

Business, a fast-paced world where "yesterday" is simply "a lot of time ago". Harnessing the power of data has become a game-changer. From analyzing customer behavior to making informed decisions, data analytics has emerged as a crucial strategy for organizations across industries. But fear not, because we have a toolkit...

Digital Adaptation Meeting

Download the "Digital Adaptation Meeting" presentation for PowerPoint or Google Slides. Gone are the days of dreary, unproductive meetings. Check out this sophisticated solution that offers you an innovative approach to planning and implementing meetings! Detailed yet simplified, this template ensures everyone is on the same page, contributing to a...

Maths for Elementary 2nd Grade - Measurement and Data

Make your elementary students have fun learning math operations, measurements and hours thanks to this interactive template. It has cute animal illustrations and a white background with a pastel purple frame. Did you notice the typography of the titles? It has a jovial touch that mimics the handwriting of a...

Statistics and Probability: Data Analysis and Interpretation - Math - 10th Grade

Download the "Statistics and Probability: Data Analysis and Interpretation - Math - 10th Grade" presentation for PowerPoint or Google Slides. High school students are approaching adulthood, and therefore, this template’s design reflects the mature nature of their education. Customize the well-defined sections, integrate multimedia and interactive elements and allow space...

Data Collection and Analysis - Master of Science in Community Health and Prevention Research

Download the "Data Collection and Analysis - Master of Science in Community Health and Prevention Research" presentation for PowerPoint or Google Slides. As university curricula increasingly incorporate digital tools and platforms, this template has been designed to integrate with presentation software, online learning management systems, or referencing software, enhancing the...

Data Analysis and Statistics - 5th Grade

Download the "Data Analysis and Statistics - 5th Grade" presentation for PowerPoint or Google Slides and easily edit it to fit your own lesson plan! Designed specifically for elementary school education, this eye-catching design features engaging graphics and age-appropriate fonts; elements that capture the students' attention and make the learning...

Simple Data Visualization MK Plan

Have your marketing plan ready, because we've released a new template where you can add that information so that everyone can visualize it easily. Its design is organic, focusing on wavy shapes, illustrations by Storyset and some doodles on the backgrounds. Start adding the details and focus on things like...

Big Data and Predictive Analytics in Healthcare Breakthrough

Have you heard about big data? This analysis system uses huge amount of data in order to discover new tendencies, perspectives and solutions to problems. It has a lot of uses in the medical field, such as prescriptive analysis, clinical risk intervention, variability reduction, standardized medical terms… Use this template...

Data Analysis and Statistics - 4th Grade

Download the "Data Analysis and Statistics - 4th Grade" presentation for PowerPoint or Google Slides and easily edit it to fit your own lesson plan! Designed specifically for elementary school education, this eye-catching design features engaging graphics and age-appropriate fonts; elements that capture the students' attention and make the learning...

Data Analysis Meeting

Choose your best outfit, bring a notebook with your notes, and don't forget a bottle of water to clear your voice. That's right, the data analysis meeting begins! Apart from everything we've mentioned, there's one thing missing to make the meeting a success. And what could it be? Well, a...

Data Science Consulting

Do you want a high-impact representation of your data science consulting company? Don’t hit the panic button yet! Try using this futuristic presentation to promote your company and attract new clients.

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