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9 Mar, 2025 6 days 15 Seats Available

In today’s data-driven world, there is an increasing need for skilled data analytics specialists. Experts in data analysis and interpretation are sought after by businesses in a variety of sectors in order to support well-informed decision-making and obtain a competitive advantage. An specialized Analysis of Data Training Course is available at KIT Training Point in Kathmandu, Nepal, if you want to improve your job chances and discover the possibilities of data. With its emphasis on offering top-notch instruction and skills that are applicable to the industry, KIT Training Point is your doorway to success in the data analytics space.

KIT Training Point offers a comprehensive Data Analysis Training course designed to equip students and professionals with the necessary skills to become proficient in data analysis and harness the power of data for decision-making. With the increasing demand for data-driven insights, this course provides the foundational knowledge and practical expertise required to excel in the field of data analysis. This course covers key areas of data analysis, including data collection, data cleaning, exploratory data analysis (EDA), statistical analysis, data visualization, and reporting. Students will be trained on industry-standard tools such as Microsoft Excel, Python, R, and SQL to manage and analyze data effectively.

Duration : 6 Day

Class Type : Physical, Online

Course Category : Digital Marketing & SEO Finance & Banking

Objectives of Course

The Data Analysis Training at KIT Training Point is designed to provide students with the essential skills and knowledge to become proficient data analysts and leverage data to drive insightful decision-making. The course objectives include:

1. Master the Fundamentals of Data Analysis

  • Equip students with a deep understanding of data analysis principles, including data collection, data cleaning, exploratory data analysis (EDA), and statistical analysis. Students will learn how to manage and analyze data efficiently.

2. Learn Key Tools for Data Analysis

  • Provide hands-on experience with essential industry tools such as Python, R, SQL, and Microsoft Excel to enable students to work with diverse datasets and perform complex analyses.

3. Develop Practical Data Preprocessing Skills

  • Teach students how to clean, transform, and preprocess raw data into usable formats. They will learn techniques to handle missing values, outliers, and inconsistencies to ensure the quality of their analysis.

4. Master Data Visualization Techniques

  • Help students develop the skills to create clear and effective data visualizations using tools like Matplotlib, Seaborn, and ggplot2, enabling them to present insights in an easily digestible format to stakeholders.

5. Apply Advanced Analytical Methods

  • Introduce advanced techniques in statistical modeling, predictive analysis, regression analysis, and time-series forecasting to help students tackle complex data problems and provide actionable insights.

6. Gain Proficiency in SQL for Data Handling

  • Provide an understanding of how to use SQL to query and manipulate data from relational databases, a critical skill for any data analyst working with large datasets stored in databases.

7. Practical Application Through Projects

  • Offer real-world projects and case studies where students can apply their skills and gain practical experience. These projects will form the foundation of a strong professional portfolio.

8. Prepare for a Successful Data Analyst Career

  • Ensure that students acquire the practical and theoretical knowledge needed to pursue a career as a data analyst or related roles. The course includes guidance on job readiness, including creating a strong portfolio and preparing for interviews.

Why Choose KIT Training Point for This Course?

Here are the key reasons to choose Data Analysis Training at KIT Training Point:

1. Comprehensive Curriculum

  • The course covers essential data analysis skills, from the basics of data collection and cleaning to advanced techniques like statistical modeling and predictive analysis. It prepares you for real-world data challenges.

2. Industry-Relevant Tools

  • Gain hands-on experience with widely-used data analysis tools such as Python, R, SQL, and Microsoft Excel. These tools are critical for data manipulation, analysis, and visualization in today’s job market.

3. Experienced Instructors

  • Learn from expert instructors with years of practical experience in data science and business analytics. Their guidance and insights will help you navigate the complex world of data analysis.

4. Real-World Application

  • Work on live datasets and real-world projects that simulate the challenges faced in professional data analysis roles. This practical approach ensures you’re job-ready from day one.

5. Data Visualization Expertise

  • Learn how to create compelling visualizations using powerful tools like Matplotlib, Seaborn, and ggplot2. These skills are essential for presenting data in a way that is understandable and actionable for business stakeholders.

6. Strong Career Opportunities

  • The demand for skilled data analysts is growing across industries. This training will give you the skills and knowledge to excel in this high-demand field, opening doors to opportunities in various sectors like finance, healthcare, marketing, and technology.

7. Job-Ready Skills

  • KIT Training Point provides career-focused training, ensuring that you not only master the technical aspects of data analysis but also learn how to effectively communicate insights, which is crucial for career advancement.

8. Personalized Attention

  • With a focus on small class sizes and hands-on learning, you receive personalized attention from instructors, ensuring a deeper understanding of the subject matter and improved learning outcomes.

9. Flexible Learning Options

  • Choose from flexible learning options that suit your schedule. Whether you’re a full-time student or a working professional, KIT Training Point offers online and in-person classes to accommodate your learning needs.

10. Build a Strong Portfolio

  • Throughout the course, you will work on projects and case studies that can be added to your portfolio, helping you stand out to potential employers in the data analytics field.
Syllabus Highlights

Data Analysis Training Syllabus (45 Days) – KIT Training Point

Total Duration: 45 Days
Total Hours: 90 Hours (2 Hours per Day)


Module 1: Introduction to Data Analysis (Day 1-5 | 10 Hours)

  • What is Data Analysis?
    • Introduction to Data Science and Data Analytics
    • Importance and Application of Data Analysis in Various Industries
    • Key Concepts: Data Collection, Data Cleaning, Data Transformation, Data Visualization
  • Data Types and Structures
    • Understanding Different Types of Data (Qualitative vs Quantitative)
    • Working with Structured and Unstructured Data
    • Data Formats: CSV, JSON, Excel, SQL
  • Tools for Data Analysis
    • Introduction to Excel, Python, R, SQL
    • Setting up the Data Analysis Environment
    • Installing Python, R, and relevant libraries (Pandas, NumPy, Matplotlib)

Module 2: Data Collection and Data Cleaning (Day 6-10 | 10 Hours)

  • Data Collection Techniques
    • Sources of Data: Surveys, Web Scraping, APIs
    • Introduction to Data Collection Tools (Google Forms, APIs, Web Scraping Tools)
  • Data Cleaning Basics
    • Handling Missing Values, Duplicates, and Outliers
    • Data Transformation and Formatting
    • Dealing with Inconsistent Data and Errors
  • Hands-on Practice: Data Cleaning in Excel and Python

Module 3: Exploratory Data Analysis (EDA) (Day 11-15 | 10 Hours)

  • What is Exploratory Data Analysis (EDA)?
    • EDA Process: Overview and Importance
    • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
    • Data Distributions and Skewness
  • Visualization Techniques in EDA
    • Introduction to Data Visualization Tools
    • Creating Histograms, Box Plots, and Bar Charts
    • Correlation and Heatmaps
    • Hands-on: EDA with Pandas, Matplotlib, and Seaborn in Python

Module 4: Statistical Analysis and Hypothesis Testing (Day 16-20 | 10 Hours)

  • Introduction to Statistics for Data Analysis
    • Probability Distributions (Normal, Poisson, Binomial)
    • Statistical Measures: Mean, Median, Mode, and Range
    • Variability and Dispersion in Data
  • Hypothesis Testing
    • Null and Alternative Hypotheses
    • T-tests, Z-tests, Chi-square Tests
    • Confidence Intervals and P-Values
  • Hands-on Practice: Statistical Analysis Using Python and R

Module 5: Data Visualization (Day 21-25 | 10 Hours)

  • Importance of Data Visualization
    • Principles of Effective Visualization
    • Storytelling with Data
  • Visualization Tools and Techniques
    • Introduction to Matplotlib, Seaborn, and Tableau
    • Visualizing Data with Line Charts, Scatter Plots, and Pie Charts
    • Advanced Visualizations: Geographical Maps, Network Graphs
  • Hands-on Practice: Creating Visualizations with Python and Tableau

Module 6: Advanced Data Analysis Techniques (Day 26-30 | 8 Hours)

  • Regression Analysis
    • Introduction to Linear Regression and Logistic Regression
    • Understanding the Relationship Between Variables
    • Regression Models and Predictions
  • Clustering and Classification
    • K-means Clustering, Hierarchical Clustering
    • Decision Trees and Random Forests
    • Support Vector Machines (SVM) and Naive Bayes
  • Hands-on Practice: Building Regression and Classification Models in Python

Module 7: SQL for Data Analysis (Day 31-35 | 8 Hours)

  • Introduction to SQL for Data Analysis
    • SQL Basics: SELECT, FROM, WHERE, GROUP BY, JOIN
    • Filtering and Aggregating Data in SQL
    • SQL Queries for Data Extraction and Analysis
  • SQL for Complex Data Analysis
    • Subqueries, Nested Queries, and Window Functions
    • Joining Multiple Tables and Handling Relationships
    • Using SQL for Data Cleaning and Transformation
  • Hands-on Practice: SQL for Data Analysis with Real-World Data

Module 8: Data Analysis with Excel (Day 36-40 | 8 Hours)

  • Excel Basics for Data Analysis
    • Introduction to Excel for Data Cleaning and Analysis
    • Functions and Formulas for Data Manipulation
    • Pivot Tables and Pivot Charts
  • Advanced Excel for Data Analysis
    • Data Visualization in Excel (Charts, Graphs, Conditional Formatting)
    • Using Excel for Statistical Analysis (ANOVA, Correlation)
    • Working with External Data Sources (Importing, Merging, Filtering)
  • Hands-on Practice: Data Analysis Using Excel

Module 9: Machine Learning for Data Analysis (Day 41-45 | 8 Hours)

  • Introduction to Machine Learning (ML)
    • What is Machine Learning?
    • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • ML Algorithms for Data Analysis
    • Linear Regression, Logistic Regression, and Decision Trees
    • K-means Clustering and Random Forest
    • Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
  • Hands-on Practice: Building ML Models Using Python (Scikit-learn)

Final Exam and Project (Day 45)

  • Final Assessment
    • Written Exam on Data Analysis Concepts
    • Practical Exam: Solving a Real-World Data Analysis Problem
  • Capstone Project
    • Apply Data Analysis Skills to a Real-World Dataset
    • Create Visualizations and Insights from the Data
    • Present Findings to the Class

Course Features

  • Course duration 10 days
  • Total Lectures 30
  • Total Students 1000
  • Certification YES

Price - 25000