Best Core Data Analyst Course Course in Dehradun
Trainingshaala is the Best Core Data Analyst Course Training center , Certification Institute in Dehradun .
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Data Analyst Course leads the chart among the best Core Java Training Courses of Dehradun. with verified certificate
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Best Core Data Analyst Course
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Learn how to build highly scalable software applications using Java and master all of its core concepts with this best Data Analyst Course for Beginners by trainingshaala and gain a globally recognized Certification and courses at a nominal fee upon successful completion
What’s in it for You?
Welcome to the ultimate Data Analyst Course, designed and delivered by seasoned industry experts with extensive experience in the tech field. Our comprehensive online Java course is meticulously crafted to equip you with mastery over the fundamentals and enable you to code like a seasoned professional.
Course Highlights
BASIC DATA ANALYST COURSE
Introduction to Python
- Introduction to python
- Environment setup & start programming
- Python Conditional Statements, Loops and File Handling
- Core Objects and Advanced Data Structures; Functions and Lambdas
- The Object Oriented Side of it
Module 3: Exploratory Data Analysis (EDA)
- Descriptive statistics: mean, median, mode, variance, etc.
- Histograms, box plots, scatter plots
- Correlation analysis and heatmaps
- Univariate and bivariate analysis
- Data visualization using Matplotlib and Seaborn
Module 1: Introduction to Data Analysis with Python
- What is data analysis?
- Overview of Python and its data analysis libraries (NumPy, pandas, Matplotlib, Seaborn)
- Setting up your Python environment
Module 2: Data Preprocessing and Cleaning
- Importing data from various sources (CSV, Excel, SQL)
- Exploring and understanding the dataset
- Handling missing data: imputation techniques
- Dealing with outliers and anomalies
- Data transformation: normalization, standardization
- Data integration and manipulation using pandas
Core DATA ANALYST COURSE Details
Introduction to Python
- Introduction to python
- Environment setup & start programming
- Python Conditional Statements, Loops and File Handling
- Core Objects and Advanced Data Structures; Functions and Lambdas
- The Object Oriented Side of it
Module 1: Introduction to Data Analysis with Python
- What is data analysis?
- Overview of Python and its data analysis libraries (NumPy, pandas, Matplotlib, Seaborn)
- Setting up your Python environment
Module 2: Data Preprocessing and Cleaning
- Importing data from various sources (CSV, Excel, SQL)
- Exploring and understanding the dataset
- Handling missing data: imputation techniques
- Dealing with outliers and anomalies
- Data transformation: normalization, standardization
- Data integration and manipulation using pandas
Module 3: Exploratory Data Analysis (EDA)
- Descriptive statistics: mean, median, mode, variance, etc.
- Histograms, box plots, scatter plots
- Correlation analysis and heatmaps
- Univariate and bivariate analysis
- Data visualization using Matplotlib and Seaborn
Module 4: Data Visualization
- Advanced data visualization techniques: bar plots, line plots, pie charts, etc.
- Interactive visualizations using Plotly
- Geospatial visualization
- Effective data storytelling and communication
Module 5: Statistical Analysis
- Sampling techniques and the Central Limit Theorem
- Hypothesis testing: t-tests, chi-square tests, ANOVA
- Confidence intervals and p-values
- Interpreting statistical results
Module 6: Machine Learning algorithms for Data Analysis
- Introduction to machine learning
- Feature engineering and selection
- Linear regression: simple and multiple regression
- Logistic regression for classification
- Decision trees and random forests
- Model evaluation metrics: R-squared, MAE, RMSE, accuracy, precision, recall, F1-score
- Model assumptions and diagnostics
Module 7: Time Series Analysis
- Introduction to time series data
- Time series components: trend, seasonality, noise
- Decomposition techniques
- Time series forecasting methods: moving average, ARIMA, exponential smoothing
- Implementing time series analysis in Python
Module 8: Advanced Topics
- Dimensionality reduction techniques (PCA, t-SNE)
- Clustering algorithms (K-means, hierarchical clustering)
- Advanced statistical techniques (non-parametric tests, ANCOVA)
Module 9: Real-World Projects and Case Studies
- Applying data analysis concepts to real datasets
- Solving data analysis challenges and problems
- Creating a portfolio of data analysis projects
7) Statics
- Static variables and methods
- Static imports
- Static initialization blocks; instance intialization blocks
- Static concept in inheritance
8) Constructors
- What are Constructors?
- Properties of Constructors
- Default and Parameterized Constructors
- Rules for constructor implementation
- Constructor Chaining
- this call; super call for constructors
- Constructors for Enumerated Data Types
- Constructors concept for Abstract classes and interfaces