Course Curriculum
| ITDS420 Data Structures and Algorithms | |||
| Data Structures and Algorithms : Module Description | 00:00:00 | ||
| Lecture 1 (Course Outline, Overview, Data Type, Abstract Data Type) | 00:00:00 | ||
| Lecture 2 (Data Structures, Problem, Input Size) | 00:00:00 | ||
| Lecture 3 (Algorithms, Proof of Correctness, Linear Search) | 00:00:00 | ||
| Lecture 4 (Comparing Algorithms, Complexity, Case-Based Analysis, Model of Computation) | 00:00:00 | ||
| Lecture 5 (Worst-Case Examples: Linear Search, Insertion Sort) | 00:00:00 | ||
| Lecture 6 (Motivation, Big-Oh, Usage, Examples, Proving “is” Claims) | 00:00:00 | ||
| Lecture 7 (Proving “is” Big-Oh Claims, proving “not is” Big-Oh Claims) | 00:00:00 | ||
| Lecture 8 (Properties of Big-Oh, Recursion (Part 1)) | 00:00:00 | ||
| Lecture 9 (Recursion (Part 2), Binary Search) | 00:00:00 | ||
| Lecture 10 (Binary Search Analysis, Stacks) | 00:00:00 | ||
| Lecture 11 (Stacks Applications and Queues) | 00:00:00 | ||
| Lecture 12 (Stack and Queue Performance, Sorting, Quick Sort (Part 1)) | 00:00:00 | ||
| Lecture 13 (Quick Sort) | 00:00:00 | ||
| Lecture 14 (Merge Sort, Dictionaries, Basics) | 00:00:00 | ||
| Lecture 15 (Dictonaries, Hashing, Hash Function, Hash Code) | 00:00:00 | ||
| Lecture 16 (Separate Chaining, Load Factor, Open Addressing (Part 1)) | 00:00:00 | ||
| Lecture 17 (Open Addressing (Part 2), Tree Terminology) | 00:00:00 | ||
| Lecture 18 (Linked Tree Representation, Properties, Computing the Height, Tree Traversals) | 00:00:00 | ||
| Lecture 19 (Ordered Dictionaries, Binary Trees, Binary Search Trees, Traversals, smallest) | 00:00:00 | ||
| Lecture 20 (Binary Search Tree Operations) | 00:00:00 | ||
| Lecture 21 (Binary Search Tree Issues, AVL Trees, Properties) | 00:00:00 | ||
| Lecture 22 (AVL Trees (Part 2), Rebalancing via Rotations, putAVL) | 00:00:00 | ||
| Lecture 23 (AVL Trees (Part 3), Multiway Search Trees (Part 1)) | 00:00:00 | ||
| Lecture 24 (Multiway Search Tree (Part 2)) | 00:00:00 | ||
| Lecture 25 (Exam, Multiway Search Trees (Part 3), (2,4)-Trees, Insertion) | 00:00:00 | ||
| Lecture 26 ((2,4)-Tree removal and more, B-Trees) | 00:00:00 | ||
| MSITDS420 Data Structures and Algorithms : Assignment | 1 month, 2 weeks | ||
| ITDS440 Statistical Analysis for Data Science | |||
| Statistical Analysis for Data Science : Module Description | 00:00:00 | ||
| Statistics Course Overview | Best Statistics Course | MarinStatsLectures | 00:00:00 | ||
| Statistics Video Tutorials at a Glance | Best Statistics Tutorials | MarinStatsLectures | 00:00:00 | ||
| Statistics Terminology and Definitions| Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Study Designs (Cross-sectional, Case-control, Cohort) | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Variables and Types of Variables | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Bar Chart, Pie Chart, Frequency Tables | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Histograms and Density Plots for Numeric Variables | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Boxplots in Statistics | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Plots for Two Variables | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Describing Distributions: Center, Spread & Shape | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Mean, Median and Mode in Statistics | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Percentiles, Quantiles and Quartiles in Statistics | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Measures of Spread & Variability: Range, Variance, SD, etc| Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Standard Deviation & Degrees of Freedom Explained | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Sample and Population in Statistics | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Normal Distribution, Z-Scores & Empirical Rule | Statistics Tutorial #3 | MarinStatsLectures | 00:00:00 | ||
| Samples from a Normal Distribution | Statistics Tutorial #4 | MarinStatsLectures | 00:00:00 | ||
| Central Limit Theorem & Sampling Distribution Concepts | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| Standard Error of the Mean: Concept and Formula | Statistics Tutorial #6 | MarinStatsLectures | 00:00:00 | ||
| Confidence Interval Concept Explained | Statistics Tutorial #7 | MarinStatsLectures | 00:00:00 | ||
| Hypothesis Testing Explained | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| t-distribution in Statistics and Probability | Statistics Tutorial #9 | MarinStatsLectures | 00:00:00 | ||
| Confidence Interval for Mean with Example | Statistics Tutorial #10 | MarinStatsLectures | 00:00:00 | ||
| Margin of Error & Sample Size for Confidence Interval | Statistics Tutorial #11| MarinStatsLectures | 00:00:00 | ||
| Bootstrapping and Resampling in Statistics with Example| Statistics Tutorial #12 |MarinStatsLectures | 00:00:00 | ||
| Hypothesis Testing: Calculations and Interpretations| Statistics Tutorial #13 | MarinStatsLectures | 00:00:00 | ||
| Hypothesis Testing: One Sided vs Two Sided Alternative | Statistics Tutorial #14 |MarinStatsLectures | 00:00:00 | ||
| Hypothesis Test vs. Confidence Interval | Statistics Tutorial #15 | MarinStatsLectures | 00:00:00 | ||
| Errors and Power in Hypothesis Testing | Statistics Tutorial #16 | MarinStatsLectures | 00:00:00 | ||
| Power Calculations in Hypothesis Testing | Statistics Tutorial #17 | MarinStatsLectures | 00:00:00 | ||
| Statistical Inference Definition with Example | Statistics Tutorial #18 | MarinStatsLectures | 00:00:00 | ||
| Bivariate Analysis Meaning | Statistics Tutorial #19 | MarinStatsLectures | 00:00:00 | ||
| Paired t Test | Statistics Tutorial #21| MarinStatsLectures | 00:00:00 | ||
| Wilcoxon Signed Rank Test | Statistics Tutorial #22 | MarinStatsLectures | 00:00:00 | ||
| Two Sample t-test for Independent Groups | Statistics Tutorial #23| MarinStatsLectures | 00:00:00 | ||
| Two Sample t-Test:Equal vs Unequal Variance Assumption| Statistics Tutorial #24| MarinStatsLectures | 00:00:00 | ||
| Bootstrap Hypothesis Testing in Statistics with Example |Statistics Tutorial #35 |MarinStatsLectures | 00:00:00 | ||
| Bootstrap Confidence Interval with Examples | Statistics Tutorial #36 | MarinStatsLectures | 00:00:00 | ||
| Permutation Hypothesis Testing with Example | Statistics Tutorial # 37 | MarinStatsLectures | 00:00:00 | ||
| One Way ANOVA (Analysis of Variance): Introduction | Statistics Tutorial #25 | MarinStatsLectures | 00:00:00 | ||
| ANOVA (Analysis of Variance) and Sum of Squares | Statistics Tutorial #26 | MarinStatsLectures | 00:00:00 | ||
| ANOVA Part III: F Statistic and P Value | Statistics Tutorial #27 | MarinStatsLectures | 00:00:00 | ||
| ANOVA Part IV: Bonferroni Correction | Statistics Tutorial #28 | MarinStatsLectures | 00:00:00 | ||
| Chi Square Test of Independence | Statistics Tutorial #29| MarinStatsLectures | 00:00:00 | ||
| Odds Ratio, Relative Risk, Risk Difference | Statistics Tutorial #30| MarinStatsLectures | 00:00:00 | ||
| Case-Control Study and Odds Ratio | Statistics Tutorial #31| MarinStatsLectures | 00:00:00 | ||
| Simple Linear Regression Concept | Statistics Tutorial #32 | MarinStatsLectures | 00:00:00 | ||
| Linearity and Nonlinearity in Linear Regression | Statistics Tutorial #33 | MarinStatsLectures | 00:00:00 | ||
| R Squared or Coefficient of Determination | Statistics Tutorial | MarinStatsLectures | 00:00:00 | ||
| MSITDS440 Statistical Analysis for Data Science : Assignment | 1 month, 2 weeks | ||
| ITDS460 Data Analytics and Visualization | |||
| Data Analytics and Visualization: Module Description | 00:00:00 | ||
| Intro (Ch 1), Visualization Analysis & Design | 00:00:00 | ||
| Data Abstraction (Ch 2), Visualization Analysis & Design | 00:00:00 | ||
| Task Abstraction (Ch 3), Visualization Analysis & Design | 00:00:00 | ||
| Nested Model (Ch 4) I, Visualization Analysis & Design | 00:00:00 | ||
| Nested Model (Ch 4) II, Visualization Analysis & Design | 00:00:00 | ||
| Marks and Channels (Ch 5), Visualization Analysis & Design | 00:00:00 | ||
| Marks and Channels (Ch 5) II, Visualization Analysis & Design | 00:00:00 | ||
| Rules of Thumb (Ch 6), Visualization Analysis & Design | 00:00:00 | ||
| Tables (Ch 7) I, Visualization Analysis & Design | 00:00:00 | ||
| Tables (Ch 7) II, Visualization Analysis & Design | 00:00:00 | ||
| Geographic Maps (Ch 8) I, Visualization Analysis & Design | 00:00:00 | ||
| Spatial Fields (Ch 8) II, Visualization Analysis & Design | 00:00:00 | ||
| Networks (Ch 9) I, Visualization Analysis & Design | 00:00:00 | ||
| Networks (Ch 9) II, Visualization Analysis & Design | 00:00:00 | ||
| Color (Ch 10) II, Visualization Analysis & Design | 00:00:00 | ||
| Color (Ch 10) III, Visualization Analysis & Design | 00:00:00 | ||
| Interactive Views (Ch 11), Visualization Analysis & Design | 00:00:00 | ||
| Multiple Views (Ch 12), Visualization Analysis & Design | 00:00:00 | ||
| Reduce: Aggregation & Filtering (Ch 13), Visualization Analysis & Design | 00:00:00 | ||
| Embed: Focus+Context (Ch 14), Visualization Analysis & Design | 00:00:00 | ||
| Wrapup, Visualization Analysis & Design, | 00:00:00 | ||
| MSITDS460 Data Analytics and Visualization : Assignment | 1 month, 2 weeks | ||
| ITDS520 Big Data Technologies and Analytics | |||
| Big Data Technologies and Analytics : Module Description | 00:00:00 | ||
| Big Data Tutorial For Beginners | 00:00:00 | ||
| Data Science for Beginner | 00:00:00 | ||
| MITDS520 Big Data Technologies and Analytics : Assignment | 1 month, 2 weeks | ||
| ITDS540 Machine Learning and Artificial Intelligence | |||
| Machine Learning and Artificial Intelligence : Module Description | 00:00:00 | ||
| Artificial Intelligence Full Course | 00:00:00 | ||
| MITDS540 Machine Learning and Artificial Intelligence : Assignment | 1 month, 2 weeks | ||
| ITDS620 Programming languages and software development | |||
| Learning Material I – Introduction to programming and computer science | 00:00:00 | ||
| Learning Material II – Software engineering | 00:00:00 | ||
| MITDS620 Machine Learning and Artificial Intelligence : Assignment | 1 month, 2 weeks | ||
| ITDS640 Programming languages and software development | |||
| ITDS640 Cloud Computing and Distributed Systems – Module Description | 00:00:00 | ||
| Learning Material I – Cloud computing | 00:00:00 | ||
| Learning Material II – Distributed Systems | 00:00:00 | ||
| MITDS640 Machine Learning and Artificial Intelligence : Assignment | 1 month, 2 weeks | ||
| ITDS660 Ethical and Legal Issues Related to Data Science | |||
| ITDS660 Ethical and Legal Issues Related to Data Science – Module Description | 00:00:00 | ||
| Learning Materials I – Ethical Dimensions of Data Science | 00:00:00 | ||
| Learning Materials II – Ethical Issues in Data Science | 00:00:00 | ||
| Learning Materials III – Ethics of Data Science | 00:00:00 | ||
| MITDS660 Machine Learning and Artificial Intelligence : Assignment | 1 month, 2 weeks | ||
| ITDS680 Capstone Project or Internship in IT and Data Science | |||
| ITDS680 Capstone Project or Internship in IT and Data Science – Module Description | 00:00:00 | ||
Instructors
57 STUDENTS ENROLLED
