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Data Analyst/Business Analyst

Data Analyst/Business Analyst training course

Topics

Turning Data into Information

Concepts in Statistics
Data Visualization
(Box Plot, Scatter Plot, Bar Chart, Line Chart etc.)
Measure of Central Tendency
(Mean, Mode, Median, Standard Deviation, Variance etc)
Measures of Variables
(About Discrete and Continuous Variables)
Covariance and Correlation
Real time Problems

Probability Distributions
1. Concepts in Probability
2. Random Variables
3. Discrete Random Variables
4. Binomial Distributions
5. Poison Distributions
6. Mean and Expected Values
7. Continuous Random Variables
8. Normal Distributions
9. Real time Problems
Sampling Distributions
1. Central Limit Theorem
2. Sampling Distributions of Sample Mean and Proportions
3. Real time Problems
Confidence Intervals
1. Construction of Confidence Interval of Population Mean
2. Construction of Confidence Interval of Population Variance
3. Statistical Inference
4. Real time Problems
Hypothesis Testing
1. Type1 and Type2 Errors
2. Decision Making in Hypothesis Testing
3. Hypothesis Testing for Population Mean
4. Hypothesis Testing for Population Variance
5. Hypothesis Testing for Population Proportion
6. Hypothesis Testing on One-Sample
7. Hypothesis Testing on Two-Samples
8. Real time Problems
Anova (Analysis of Variance)
1. ANOVA Assumptions
2. One-way and Two-way ANOVA
3. Multiple Comparisons (Tukey, Dunnett Methods)
4. Real time Problems
Regression Analysis/ Predictive Modelling
1. Concepts of Simple Linear Regression
2. Concepts of Multiple Linear Regression
3. Coefficient of Determination
4. Multicollinearity
5. Real time Examples
Machine Learning (ML)
1. Introduction
2. Application Examples
3. Supervised Learning
4. Unsupervised Learning
Cluster Analysis
1. Agglomerative Hierarchical Clustering
2. K-Means Procedure
3. Medoid Cluster Analysis
Association Rules
1. Market Basket Analysis
2. Apriori/Support/Confidence/Lift
3. Real time Problems
Tree based Methods
1. Basics of Decision Trees
2. Regression Trees
3. Classification Trees
4. Ensemble Methods
5. Bagging, Bootstrap, Random Forests Boosting
6. Using software-Real time Problems
Logistic Regression
Naïve Bayes (Natural Langauge Processisng)
Neural Networks

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