Best Data Analytics Course in kerala

A Whole Guide to Data Analytics Course : Learn skills that are in demand and become equipped for work in Kerala.

What you'll learn

In today’s data-driven world, the ability to extract insights from information is crucial. Our comprehensive Data Analytics Course equips you with the skills to transform raw data into actionable knowledge, empowering you to make informed decisions and solve real-world problems.

Whats Material Includes?

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Course Curriculum: Your Path to Data Analytics Expertise

Our Data Analytics Course is meticulously crafted to equip you with the essential skills to become a proficient data analyst. Here’s a glimpse into the exciting world of data analytics that awaits you:

Introduction to Data Analytics

Module 1: Introduction to Data Analytics

overview of data analytics and its significance in modern business and decision-making processes
Understanding data analysts’ roles and responsibilities in extracting insights from data

Module 2: Foundations of Data Analysis

Exploring the data analysis process: defining questions, collecting data, cleaning data, analyzing data, and interpreting results
Understanding the importance of data quality and integrity in analysis

Module 3: Types and Sources of Data

Differentiating between structured and unstructured data
Exploring various data sources: databases, spreadsheets, text files, web scraping, and APIs

Module 4: Data Exploration and Visualization

Introduction to data exploration techniques: summary statistics, data profiling, and data visualization
Using tools like Excel, Python, and Tableau for data visualization

Module 5: Descriptive Analytics

Analyzing and summarizing data using descriptive statistical techniques
Data distributions, central tendency, and variability measures generate insights

Module 6: Inferential Analytics

Introduction to inferential statistical techniques for making predictions and drawing conclusions from data samples
Hypothesis testing, confidence intervals, and regression analysis

Module 7: Predictive Analytics

Understanding predictive modeling techniques for forecasting future trends and outcomes
Building predictive models using machine learning algorithms

Module 8: Prescriptive Analytics

Exploring prescriptive analytics methods for recommending actions and decisions based on data insights
Optimization techniques, decision trees, and scenario analysis

Module 9: Data Analytics Tools and Software

Overview of popular data analytics tools and software: Excel, R, Python, SQL, Tableau, and Power BI
Selecting the right tool for specific data analysis tasks and requirements

Module 10: Ethical and Legal Considerations in Data Analytics

Understanding ethical concerns about data privacy, security, and bias in data analysis
Compliance with data protection laws and regulations, such as GDPR and CCPA

This course curriculum provides a comprehensive introduction to data analytics, covering foundational concepts, techniques, tools, and ethical considerations essential for aspiring data analysts and professionals seeking to leverage data for decision-making and business insights.

Data Collection and Cleaning

Module 1: Introduction to Data Collection

An overview of the data collection process and its importance in data analysis.
Understanding different sources and methods of data collection: surveys, interviews, observations, and digital tracking

Module 2: Planning Data Collection

Defining research questions and objectives to guide data collection efforts
Designing data collection instruments: questionnaires, surveys, and interview guides

Module 3: Sampling Methods

Introduction to sampling techniques: random sampling, stratified sampling, cluster sampling, and convenience sampling
Understanding sample size determination and its implications for data analysis.

Module 4: Data Collection Tools and Technologies

Overview of data collection tools and technologies: online survey platforms, mobile data collection apps, and sensor-based devices
Selecting the appropriate data collection tool based on research objectives and target population

Module 5: Data Cleaning Process

Understanding the importance of data cleaning in preparing data for analysis
Identifying common data quality issues: missing values, duplicate records, outliers, and inconsistencies

Module 6: Data Cleaning Techniques

Exploring techniques for identifying and handling missing data: imputation, deletion, and interpolation
Dealing with duplicate records: detecting and removing duplicate entries from datasets

Module 7: Handling Outliers and Anomalies

Identifying outliers and anomalies in data distributions
Techniques for handling outliers: filtering, transformation, and robust statistical methods

Module 8: Data Standardization and Normalization

Standardizing data formats and units to ensure consistency across datasets
Normalizing data to a common scale for comparison and analysis

Module 9: Data Validation and Quality Assurance

Implementing data validation checks to identify errors and inconsistencies
Establishing quality assurance procedures to ensure data accuracy and reliability

Module 10: Documenting the Data Cleaning Process

Creating documentation and metadata to track data cleaning steps and transformations
We are documenting decisions and rationale for data cleaning procedures to ensure reproducibility and transparency.

Data Analysis Techniques

Module 1: Introduction to Data Analysis

An overview of data analysis and its role in extracting insights from data
Understanding the data analysis process: defining objectives, data preparation, analysis, interpretation, and visualization

Module 2: Exploratory Data Analysis (EDA)

Introduction to EDA techniques for understanding data characteristics and patterns
Exploring summary statistics, data distributions, and visualization methods

Module 3: Statistical Analysis Methods

Overview of statistical analysis techniques for hypothesis testing and inference
Understanding parametric and non-parametric tests: t-tests, ANOVA, chi-square tests, and correlation analysis

Module 4: Regression Analysis

An introduction to regression analysis for modeling relationships between variables
We use techniques such as linear regression, multiple regression, and logistic regression.

Module 5: Time Series Analysis

Exploring time series analysis methods for analyzing temporal data
Time series decomposition, trend analysis, and forecasting techniques

Module 6: Machine Learning Algorithms

Overview of machine learning algorithms for predictive modeling and classification
Supervised learning algorithms: decision trees, random forests, support vector machines (SVM), and neural networks

Module 7: Cluster Analysis

Introduction to cluster analysis techniques for grouping similar data points
We employ K-means clustering, hierarchical clustering, and DBSCAN clustering methods.

Module 8: Factor Analysis and Dimensionality Reduction

Understanding factor analysis techniques for identifying underlying factors in datasets
Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction

Module 9: Text Mining and Sentiment Analysis

An introduction to text mining techniques for analyzing unstructured text data
We employ sentiment analysis, topic modeling, and text classification techniques.

Module 10: Spatial Analysis and Geographic Information Systems (GIS)

Exploring spatial analysis techniques for analyzing geographical data
Overview of GIS software and tools for mapping, spatial querying, and spatial data analysis

Module 4: Data Visualization with Tools like Tableau

Delve into data visualization and explore tools like Tableau to create compelling charts and graphs that effectively communicate insights from your data. (May vary depending on the course content).

Introduction to Statistics and Probability

Gain a foundational understanding of statistical concepts and probability theory, essential for analyzing data and drawing meaningful conclusions.

Programming for Data Analysis with Python

Module 1: Introduction to Python for Data Analysis

Overview of the Python programming language and its popularity in data analysis
Setting up a Python environment: installing Python, Anaconda, and Jupyter Notebook

Module 2: Python Basics for Data Analysis

Introduction to Python syntax: variables, data types, operators, and basic operations
Understanding control flow: if statements, loops, and functions

Module 3: Working with Data Structures in Python

Introduction to Python data structures: lists, tuples, dictionaries, and sets
Exploring NumPy arrays for numerical data manipulation and operations.

Module 4: Data Manipulation with Pandas

Introduction to the Pandas library for data manipulation and analysis
Loading and inspecting datasets: reading CSV, Excel, and JSON files

Module 5: Data Cleaning and Preprocessing

Handling missing data: detecting, removing, and imputing missing values
Data preprocessing techniques: standardization, normalization, and feature scaling

Module 6: Exploratory Data Analysis (EDA)

Visualizing data with Matplotlib and Seaborn: line plots, scatter plots, histograms, and box plots
Analyzing data distributions, correlations, and relationships

Module 7: Statistical Analysis with Python

Performing statistical analysis: calculating descriptive statistics, hypothesis testing, and correlation analysis
Introduction to statistical libraries: SciPy and StatsModels

Module 8: Introduction to Machine Learning

An Overview of Machine Learning Concepts and Types of Machine Learning Algorithms
Introduction to the scikit-learn library for machine learning in Python

Module 9: Supervised Learning Algorithms

Introduction to supervised learning algorithms: linear regression, logistic regression, decision trees, and random forests
Model evaluation and validation techniques: cross-validation, metrics for regression, and classification

Module 10: Unsupervised Learning Algorithms

Introduction to unsupervised learning algorithms: clustering (K-means, hierarchical), dimensionality reduction (PCA)
Applying unsupervised learning techniques for data exploration and pattern discovery

Module 11: Introduction to Deep Learning with TensorFlow and Keras

Overview of deep learning concepts and neural networks
Introduction to TensorFlow and Keras libraries for building and training neural networks

Module 12: Data Analysis Projects

Applying Python programming and data analysis techniques to real-world datasets
Completing hands-on data analysis projects to demonstrate the skills learned throughout the course

Module 7: Machine Learning Fundamentals (Optional):

Get introduced to the core concepts of machine learning and explore its applications in data analysis, such as data mining and predictive modeling. (May vary depending on the course content).

Module 8: Data Wrangling with Libraries like Pandas

Learn powerful libraries like Pandas in Python for efficient data manipulation, cleaning, and analysis. (May vary depending on the course content).

Module 9: Data Communication and Storytelling

Master the art of communicating data insights effectively, including data storytelling techniques to present your findings to both technical and non-technical audiences.