Hey hustlers, Very good morning, and I hope you're doing great.
Welcome Day 4 of the 𝗛𝘂𝘀𝘁𝗹𝗲𝗿𝘀𝗹𝗲𝗿𝘀 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝘀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 challenge.
Today we will learn the complete statistics and mathematics topic-wise list required for the data/business analyst role.
1️⃣ Descriptive Statistics:
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation)
- Percentiles and quartiles -Skewness and kurtosis
- Histograms and frequency distributions
2️⃣ Probability: -Basic concepts of probability (sample space, events, outcomes)
- Probability rules (addition, multiplication)
- Conditional probability
- Bayes' theorem -Probability distributions (binomial normal, Poisson)
3️⃣ Inferential Statistics
- Hypothesis testing (null and alterative hypotheses) Types of errors (Type I and Type I errors)
- Confidence intervals
- Sampling techniques (random sampling, stratified sampling)
- Central Limit Theorem
- t-tests (paired and unpaired)
- Analysis of variance (ANOVA)
- Chi-square tests
4️⃣ Regression Analysis
- Simple linear regression
- Multiple linear regression
- Assumptions of regression analysis
- Model evaluation (R-squared, adjusted R-squared, residual analysis)
- Interpreting regression coefficients
- Logistic regression (binary and multinomial)
5️⃣ Time Series Analysis:
- Time series components (trend, seasonality, cyclicality noise)
- Smoothing techniques (moving averages, exponential smoothing)
- ARIMA models (AutoRegressive Integrated Moving Average)
- Forecasting methods (exponential smoothing, ARIMA trend analysis)
6️⃣ Data Visualization:
- Charts and graphs (bar charts, line charts, scatter plots, histograms)
- Box plots
- Heatmaps
- Data dashboards
- Effective data visualization principles
- Using tools like Excel, Tableau, or Python libraries (Matplotlib, Seaborn)
7️⃣ Linear Algebra
- Matrix operations (addition, subtraction, multiplication)
- Systems of linear equations
- Determinants
- Eigenvalues and eigenvectors
8️⃣ Optimization
- Linear programming
- Integer programming
- Non-linear programming
- Constraint optimization
9️⃣ Discrete Mathematics
- Set theory
- Combinatorics (permutations, combinations)
- Probability and counting principles
- Graph theory (networks, paths, connectivity)
🔟 Data Manipulation and Analysis
- Data cleaning and preprocessing
- Exploratory data analysis (EDA)
- Data transformation (normalization, standardization)
- Database querying (SQL)
- Data wrangling using tools like Python (NumPy, Pandas)
You might feel why I'm keeping on telling you to focus on math and statistics fundamentals because in data analytics we have a focus on data, which requires more math to solve problems, and even today, while solving any problems, I've got to learn some new things from the basics that we might have learned during our school, college, or university days. So do focus on understanding these foundations thoroughly and try to make notes while learning using the old books that you're carrying with you.
Use these resources and once again go through these things to clear your concepts.
https://www.youtube.com/watch?v=o7WkGYIPUM8&list=PLunlGNVWDAaY7AeDDzTeu4-DD3g7zmAXs
W3School Statistics Tutorial:
https://www.w3schools.com/statistics/
https://www.youtube.com/playlist?list=PLunlGNVWDAaY7AeDDzTeu4-DD3g7zmAXs
Looking forward to your responses.