Data Science Course

Data science is a field that applies principles and techniques of data analysis, machine learning, and statistics to gain insights and understanding from data-related events. In today’s job market, many aspire to become data scientists, making data science training one of the most popular courses to pursue.

Regardless of the industry, employers are actively seeking skilled data scientists who can provide valuable business insights. Consequently, it is currently one of the most sought-after courses, with companies willing to offer substantial salaries to individuals who have undergone data science training. Data science is also used to analyze historical data and predict potential risks for companies, enabling proactive risk mitigation.

Numerous online websites and offline coaching centers offer data science training. Online training institutes like JNNC Technologies stand out for providing high-quality training aligned with industry requirements, experienced trainers, real-world industry projects, and certification.

The training also covers visualization and reporting tools. Those who cannot commit to regular training sessions, including working professionals looking to change careers, can benefit from self-paced data science learning opportunities.

This self-learning approach features an updated curriculum in line with current industry needs and best practices, and it includes the guidance of experienced data science professionals who troubleshoot real-world data science issues.

Can you acquire data science skills at your own pace through online training? Yes, you can certainly learn data science through self-study. There are online resources and videos that can provide foundational knowledge about data science.

However, self-learning online can be a bit challenging. Fortunately, online courses like the one offered by JNNC Technologies provide a supportive environment with online trainers available to assist with any queries.

The data science self-learning online course is designed especially for busy individuals who cannot attend physical data science classes. This self-paced program accelerates the learning process and is suitable for working professionals who wish to take charge of their learning journey.

Curriculum:

• Module 1: Statistics and Probability
• a) Descriptive Statistics:
• Central tendency: Mean, Median, Mode
• Sample variance
• Standard deviation
• Random Variables: Discrete, Continuous
• Probability density functions
• Binomial distribution
• Expected Value, E(X)
• Poisson Process
• Law of large numbers
• Standard normal distribution and empirical rule
• Z-score
• b) Inferential Statistics:
• Central limit theorem
• Sampling distribution of the sample mean
• Standard error of the mean
• Mean and variance of Bernoulli distribution
• Margin of error 1
• Margin of error 2
• Confidence interval
• Hypothesis testing and p-value
• One-tailed and two tailed tests
• Z-statistics and T-statistics
• Type 1 error
• Squared error of regression line
• Co-efficient of determination
• Chi-square distribution
• Pearson’s chi square test (goodness of fit)
• Co-relation and casualty.
• Module 2: Data Analysis using Numpy and Pandas
• 1. Numpy
• Numpy Numpy Vector and Matrix
• Functions – arrange(), zeros(), ones(), linspace(), eye (),
• Reshape(), random(), max(), min(),
• argmax(), argmin(), shape and dtype attribute
• Indexing and Selection
• Numpy Operations – Array with Array, Array with Scalars,
• Universal Array Functions
• 2.Pandas
• Pandas Series
• Pandas Data-Frame
• Missing Data (Imputation)
• Group by Operations
• Merging, Joining and Concatenating Data-Frame.
• Pandas Operations
• Data Input and Output from wide variety of formats like csv, excel, db and html etc.
• Module 3: Data Visualization using Matplotlib, Seaborn, Pandas-in built, Plotly and Cufflinks
• 1.Matplotlib
• plot() using Functional approach
• multi-plot using subplot()
• plt.figure() using OO API Methods
• add_axes(), set_xlabel(), set_ylabel(), set_title() Methods
• Customization – figure size, impoving dpi, Plot appearance,
• Markers, Control over axis appearance and special Plot Types
• 2.Seaborn
• Distribution Plots using distplot(), jointplot(), pairplot(), rugplot(),
• kdeplot()
• Categorical Plots using barplot(), countplot(), boxplot(), violinplot(),
• stripplot(), swarmplot(), factorplot()
• Matrix Plots using heatmap(), clustermap()
• Grid Plots using PairGrid(), FacetGrid()
• Regression Plots using lmplot()
• Styles and Colors customization.
• 3. Plotly and Cufflinks
• Interactive Plotting using Plotly and Cufflinks
• 4.Pandas Built-in
• Histogram, Area Plot, Bar Plot, Scatter Plot, Box-plot, Hex-plot, Kde-plot, Density Plot e. Choropleth Maps
• Interactive World Map and US Map using Plotly and Cufflinks Module
• Module 4: GIT
• Distribution Version Control System
• How internally, GIT Manages Version Control on Changesets.
• Creating Repository
• Basic Commands like, git status, git add, git remove, git branch, git checkout, git log, git cat-file, git pull, git push, git commit
• Managing Configuration – System Level, User Level, Repository level
• Module 5: Jupyter Notebook
• Introduction, Basic Commands, Keyboard Shortcut and Magic Functions
• Module 6: Linear Algebra and Calculus
• Vector and Matrix, basic operations
• Trigonometry
• Derivatives
• Module 7: SQL
• MySQL Server and Client Installation
• SQL Queries
• CRUD Operations
• Module 8: Big Data
• What is big data?
• What is distributed computing?
• What is parallel processing?
• Why data scientist require big data?
• Module 9: Machine Learning Introduction
• What is Machine Learning
• Machine Learning Process Flow-Diagram
• Different Categories of Machine Leaning – Super- vised, Unsupervised and Reinforcement
• Scikit-Learn Overview
• Scikit-Learn cheat-sheet
• Module 10: Regression
• Linear Regression
• Robust Regression (RANSAC Algorithm)
• Exploratory Data Analysis (EDA)
• Correlation Analysis and Feature Selection
• Performance Evaluation – Residual Analysis, Mean Square Error (MSE), Co-efficient
• Determination R^2, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)
• Polynomial Regression
• Regularized Regression – Ridge, Lasso and Elas- tic Net Regression
• Bias-Variance Trade-Off
• Cross Validation – Hold Out and K-Fold Cross Validation
• Data Pre-Processing – Standardization, Min-Max, Normalization and
• Binarization
• Gradient Descent