At 7itech Solutions, we specialize in providing top-notch training and development programs for individuals looking to kickstart their career in Data Science. It involves the use of advanced analytics, machine learning algorithms, and artificial intelligence to gain insights from data.
A full-stack developer may manage the entire development cycle from the consumer interface to server-side and database interfaces because they are experts in each front-end and lower back-end technology.
Our team of dedicated trainers consists of industry professionals with years of experience in Data Science. They bring their practical knowledge and real-world expertise into the classroom, ensuring that you gain insights from the best minds in the field.
At 7iTech Solutions, we offer industry-relevant certification courses designed to equip learners with the technical skills and credentials needed for today’s job market. Each course includes hands-on projects, expert mentorship, and a recognized certificate upon completion.
✔️ What is Data Science?
✔️ Roles of a Data Scientist
✔️ Real-Life Applications of Data Science
✔️ Workflow and Project Lifecycle
✔️ Python Basics: Variables, Data Types, Loops, Functions
✔️ Working with Lists, Tuples, Dictionaries
✔️ File Handling
✔️ Libraries: Numpy, Pandas Overview
✔️ Hands-on Coding Exercises
✔️ Reading and Writing Data with CSV/Excel
✔️ Data Cleaning: Handling Missing Values, Duplicates
✔️ Data Manipulation (merge, groupby, pivot tables)
✔️ Sorting, Filtering, and Indexing
✔️ Introduction to Matplotlib & Seaborn.
✔️ Plotting Line, Bar, Pie, Histogram, Scatter Plots.
✔️ Styling and Annotating Charts.
✔️ Interactive Visualizations using Plotly.
✔️ Descriptive Statistics: Mean, Median, Mode, Std Dev.
✔️ Probability Theory Basics.
✔️ Normal Distribution, Z-score.
✔️ Hypothesis Testing, P-value.
✔️ Correlation vs Causation.
✔️ Basics of SQL Queries
✔️ Filtering, Sorting, Grouping, Joins
✔️ Working with Databases
✔️ Connecting SQL with Python
✔️ Types of Machine Learning (Supervised vs Unsupervised)
✔️ ML Workflow and Algorithms
✔️ Scikit-learn Overview
✔️ Model Evaluation Techniques (Train/Test Split, Accuracy, Confusion Matrix)
✔️ Linear Regression
✔️ Logistic Regression
✔️ Decision Trees & Random Forest
✔️ K-Nearest Neighbors (KNN)
✔️ Support Vector Machines (SVM)
✔️ K-Means Clustering
✔️ Hierarchical Clustering
✔️ Principal Component Analysis (PCA)
✔️ Association Rule Learning
✔️ Real-World Dataset Selection
✔️ Data Cleaning, EDA, Model Building
✔️ Project Documentation & Presentation
✔️ GitHub Portfolio Upload
