Data/Business Analyst
This Data Analysis Certification course helps you start your career as Data Analyst.
Course Contents
Businesses need data analysis more than ever. In this learning path, you will learn about the life and journey of a data analyst, the skills, tasks, ….
DATA ANALYST/BUSINESS ANALYST
DATA ANALYST/BUSINESS ANALYST
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
- Concepts in Probability
- Random Variables
- Discrete Random Variables
- Binomial Distributions
- Poison Distributions
- Mean and Expected Values
- Continuous Random Variables
- Normal Distributions
- Real time Problems
- Sampling Distributions
- Central Limit Theorem
- Sampling Distributions of Sample Mean and Proportions
- Real time Problems
- Confidence Intervals
- Construction of Confidence Interval of Population Mean
- Construction of Confidence Interval of Population Variance
- Statistical Inference
- Real time Problems
- Hypothesis Testing
- Type1 and Type2 Errors
- Decision Making in Hypothesis Testing
- Hypothesis Testing for Population Mean
- Hypothesis Testing for Population Variance
- Hypothesis Testing for Population Proportion
- Hypothesis Testing on One-Sample
- Hypothesis Testing on Two-Samples
- Real time Problems
- Anova (Analysis of Variance)
- ANOVA Assumptions
- One-way and Two-way ANOVA
- Multiple Comparisons (Tukey, Dunnett Methods)
- Real time Problems
- Regression Analysis/ Predictive Modelling
- Concepts of Simple Linear Regression
- Concepts of Multiple Linear Regression
- Coefficient of Determination
- Multicollinearity
- Real time Examples
- Machine Learning (ML)
- Introduction
- Application Examples
- Supervised Learning
- Unsupervised Learning
- Cluster Analysis
- Agglomerative Hierarchical Clustering
- K-Means Procedure
- Medoid Cluster Analysis
- Association Rules
- Market Basket Analysis
- Apriori/Support/Confidence/Lift
- Real time Problems
- Tree based Methods
- Basics of Decision Trees
- Regression Trees
- Classification Trees
- Ensemble Methods
- Bagging, Bootstrap, Random Forests Boosting
- Using software-Real time Problems
- Logistic Regression
- Naïve Bayes (Natural Langauge Processisng)
- Neural Networks
JOB Oriented Training
Interview questions
Interview tips
Resume preparation tips
