Browse Course
Job searching platforms, including BDjobs and Linkedin, offer a great number of jobs for developers. Your technical expertise in this field, along with the certification, ensures your credibility on the global market. Our career placement department assists you in this process to achieve your goals.
Course Overview
Our course is detail-oriented and high-energy individual with strong planning and organizational skills. Experience working under lead data scientist and other team members to create and implement scalable cloud-based data analytic solutions in fast-paced environments with changing priorities.
Admission Is Going On
Enroll now to any of our Offline (On-Campus) or Online (Live Class) courses as per your suitable time.
Course Curriculum
-
- Module#01: Excel for being a Data Analyst
- 1. Introduction to Excel
- 2. Beginner to Advance Excel Formula & Function
- 3. Beginner to Advance Excel Formulas & Functions
- 4. Pivot Table
- 5. Excel Charts & Graphs
- 6. Projects
- Module#02: Power BI for Mastering Business Intelligence
- 1. Power BI Main Interface and Power Query
- 2. Power Query Continued.
- 3. Data Modeling in Power BI
- 4. Power BI Chart and Graphs
- 5. DAX Basic to Advance in Power BI
- 6. Projects
- Module#3: SQL Fundamentals for Data Analytics (SQL-1)
- 1. Introduction to RDBMS: Understanding relational databases and their importance in
- analytics.
- 2. Database Normalization: Ensuring data integrity and optimization for analysis.
- 3. NoSQL Databases: Overview of NoSQL and when it might be useful in analytics.
- 4. SELECT Statements: How to pull specific data from large databases.
- 5. Using WHERE Clauses: Filtering data to analyze subsets.
- 6. Sorting Data: Using DISTINCT, TOP, and LIKE to refine analysis.
- 7. SQL Syntax Modifications: Practical applications for data modification.
- **Assignment #11: Practice SQL commands for selecting, filtering, and sorting data.
- Module#4: Advanced SQL for Data Analytics (SQL-2)
- 1. Join Types in SQL: INNER, LEFT, RIGHT, and FULL OUTER joins for merging tables
- in analysis.
- 2. Intro to NumPy for Data Analysis: Setting up NumPy as an SQL companion for
- numerical analysis.
- ***Assignment #12: Exercises using join queries to combine and analyze data.
- Module#5: Data Analysis Using NumPy
- 1. NumPy Overview: Intro to NumPy for efficient data manipulation.
- 2. Installing NumPy: Setup and environment management.
- 3. Working with NumPy Arrays: Array operations for analysis.
- 4. Methods and Attributes: Essential array methods.
- 5. Indexing and Slicing: Extracting data subsets for focused analysis.
- 6. Broadcasting and Layout: Advanced array operations.
- 7. Boolean Masking: Filtering data using logical conditions.
- 8. Arithmetic Operations & Universal Functions: Advanced data manipulation for
- analytics.
- 9. Exercise Overview and Solutions: Practice essential NumPy functions.
- ***Assignment #13: Perform data analysis using NumPy.
- Module#6: Pandas for Data Analytics (Part 1)
- 1. Intro to Pandas: Setup and capabilities of Pandas in analytics.
- 2. Series and DataFrames: Core data structures for data manipulation.
- 3. Creating and Modifying DataFrames: Data organization in Pandas.
- ***Assignment #14: Exercises on Series and DataFrames for data handling.
- Module#7: Advanced Pandas for Data Analytics (Part 2)
- 1. Hierarchical Indexing: Organizing and structuring data.
- 2. Handling Missing Data: Techniques for cleaning datasets.
- 3. Common Pandas Methods: Useful operations for data analysis.
- ***Assignment #15: Perform data wrangling with real-world datasets.
- Module#8: Data Analysis Project with NumPy and Pandas
- 1. Project Overview: Hands-on project using a Kaggle dataset.
- 2. Data Cleaning and Preparation: Apply data wrangling techniques.
- 3. Data Analysis and Visualization: Utilize NumPy and Pandas.
- ***Assignment #16: Complete analysis on a dataset to draw insights.
- Module#9: Exploratory Data Visualization with Matplotlib
- 1. Multiple Plots on Single Canvas: Analyzing multiple datasets simultaneously.
- 2. Object-Oriented Approach: Customizing plots for detailed insights.
- 3. Inset Plots and Subplots: Comparative analysis techniques.
- 4. Saving Figures: Exporting visualizations for presentations.
- ***Assignment #19: Create multi-plot visuals for an exploratory data analysis.
- Module#10: Exploratory Data Visualization with Matplotlib and Pandas
- 1. Pandas Built-in Visualization: Quick insights directly from data.
- 2. Using Style Sheets: Enhancing readability of charts.
- 3. Exploring Various Charts: Bar, histogram, line, scatter, and box plots for data pattern
- identification.
- 4. Advanced Charts: KDE and hexbin plots for density-based analysis.
- ***Assignment #20: Generate exploratory visuals to summarize a dataset.
- Module#11: Data Visualization with Seaborn
- 1. Distribution and Regression Plots: Discover data distributions and relationships.
- 2. Pair and Joint Plots: Comparative visualizations of variables.
- 3. Categorical Plots: Analysis of category-based data.
- 4. Heatmaps and Matrix Plots: Representing correlations and patterns.
- 5. Seaborn Styling: Enhancing clarity for professional presentations.
- ***Assignment #21: Perform exploratory and statistical plotting on sample dataset
This Course is Designed for
Anyone interested to learn freelancing
Job seekers
Students
Anyone interested to learn Data Analytics
Career Opportunities
Freelancing can be your first priority if you want to pursue an independent and flexible career. Many countries offer loads of work on platforms including Freelancer.com, Upwork, Themeforest where you can work using the skills.
Exclusive Solutions That Set Us Apart
Online Live Batch
Do you live abroad or prefer a remote learning process? We have launched online batches with all the offline facilities so that you can keep up with the technical advancement of today’s world. Now you can enroll in any course from anywhere, at any time.
Review Class
Do you face difficulty when you review the previous concepts? To ensure the best learning outcome, we arrange review classes that help our students overcome any problem in their skill development process. You will be able to understand the topics that you find complex under the close supervision of our skilled mentors.
Practice lab support
We offer our students practice lab support so that they can complete their courseworks feasibly at any time. The uninterrupted learning environment that we ensure helps the student gather practical knowledge in an efficient manner.
Class Videos
No need to worry if you miss a topic in the class. We record most of our classes so that students who miss a session can still get the information they need. They can watch the videos again and again until they understand the topic thoroughly. Our motto is to provide you a flexible learning experience to gradually improve your competence.
Career Placement Support
Our career placement department is ready to help you find a lucrative job. We ensure your resume gets into the hands of the right hiring manager. So far, this department has helped more than 16000 students to find jobs in competitive global platforms. Promising a better future, we have successfully raised the job placement rate to 66% in 2023.
Virtual Internship
Without in-hand experience, no one can be competent in any skill. Practical work experience is a must-have for better career opportunities. CIT offers its students virtual internship opportunities, where they can work under the supervision of industry experts. The online internships qualify to be as effective as offline work experience. Hence, you can also complete our internship at our office.