Data Science Training

• Understanding Business problems and Data understanding
• Data cleaning, data preparation, EDA, Feature Engineering
• Data Mining
• Model Evaluation
• Model Deployment
• Monitoring & Maintenance

• Topics
• Data Types
• Measure Of central tendency
• Measures of Dispersion
• Graphical Techniques
• Skewness & Kurtosis
• Box Plot
• R language
• R Studio
• Descriptive Stats in R
• Python Programming (Installation & basic commands) Libraries
• Jupyter notebook
• Set up Github
• Descriptive Stats in Python
• Pandas & Matplotlib / Seaborn

• Topics
• Random Variable
• Probability
• Probability Distribution
• Normal Distribution
• Standard Normal Distribution (SND)
• Expected Value
• Sampling Funnel
• Sampling Variation
• Central Limit Theorem (CLT)
• Confidence interval
• Assignments Session
• Introduction to Hypothesis Testing
• Hypothesis Testing with examples
• Proportion tests
• Sample t-test
• Anova & Chisquare case studies

• Visualization
• Data Cleaning
• Imputation Techniques
• Data Visualizing Plots
• Correlation analysis
• Transformations
• Normalization & Standardization

• Topics
• Encoding Methods
• One Hot Encoding (OHE)
• Label Encoders
• Mean Frequency Encoding
• Mean Encoding
• Frequency Encoding
• Outlier Detection
• Predictive Power Score

• Topics
• Recursive Feature Elimination
• Principle Component Analysis (PCA)
• Latent Dimension Analysis (LDA)

• Topics
• Splitting data into train & test
• Methods of cross-validation
• Accuracy methods

• Classification models
• Shallow machine learning models
• Ensemble models
• Regression models
• Blackbox techniques

• Topics
• Principles of Regression
• Introduction to Simple Linear Regression
• Multiple Linear Regression
• MSE (mean squared error)
• RMSE (root mean squared error)
• R2score
• Adjusted R2score

• Topics
• Principles of Logistic Regression
• Introduction to Logistic Regression
• Logistic Regression
• Confusion matrix
• False Positive, False Negative
• True Positive, True Negative
• Sensitivity, Recall, Specificity, F1 score
• Receiver operating characteristics curve (ROC curve)

• Lasso Regression
• Ridge Regression

• Topics
• Deciding the K value
• Split the data and build the KNN model.
• Understanding the various generalization, regulation techniques to avoid overfitting and underfitting
• Kernel tricks

• Topics
• Classification Tree Elements - Root node, Child Node, Leaf Node & more.
• Greedy algorithm
• Measure of Entropy
• Attribute selection using Information Gain
• Implementation of a Decision tree using C5.0 & Scikit learn libraries

• Topics
• Probability – Recap
• Bayes Rule
• Naive Bayes Classifier
• Text Classification using Naive Bayes

• Topics
• Bagging
• Boosting
• Random Forest
• XGBM
• LGBM

• Topics
• Data Mining Process
• Hierarchical Clustering / Agglomerative Clustering
• Measure of distance
• Numeric - Euclidean, Manhattan, Mahalanobis
• Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient & more.
• Mixed - Gower’s General Dissimilarity Coefficient
• Types of Linkages
• Single Linkage / Nearest Neighbour
• Complete Linkage / Farthest Neighbour
• Visualization of clustering algorithm using Dendrogram

• Topics
• R shiny
• Streamlit

• Topics
• PCA
• Why dimension reduction
• Advantages of PCA
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra

• Topics
• What is Market Basket / Affinity Analysis?
• A measure of association rules
• Support
• Confidence
• Lift Ratio
• Apriori Algorithm

• User-based collaborative filtering
• The measure of distance/similarity between users
• Driver for recommendation
• Computation reduction techniques
• Search based methods to item collaborative filtering
• Vulnerability of recommender systems

• Topics
• Sentiment Extraction
• Lexicons & Emotion Mining

• Topics
• Artificial Neural Network
• Biological Neuron vs Artificial Neuron
• ANN structure
• Activation function
• Network Topology
• Classification Hyperplanes
• Best fit “boundary”
• Gradient Descent
• Stochastic Gradient Descent Intro
• Back Propagation
• Introduction to concepts of CNN

• Topics
• Introduction to time series data
• Steps of forecasting
• Components of time series data
• Scatter plot & Time Plot
• Lag Plot
• ACF - Auto-Correlation Function / Correlogram
• Visualization principles
• Naive forecast methods
• Forecast errors and its metrics errors
• Model-Based approaches
• Linear Model
• Exponential Model
• Quadratic Model
• Additive Seasonality
• Multiplicative Seasonality
• Model-Based approaches
• AR (Auto-Regressive) model for errors
• Random walk
• ARMA (Auto-Regressive Moving Average), Order p & q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d & q
• A data-driven approach to forecasting
• Smoothing techniques
• Moving Average
• Simple Exponential Smoothing
• Holts / Double Exponential Smoothing
• Winters / HoltWinters
• De-seasoning and de-trending
• Forecasting using Python & R

The demand for a data scientist is rapidly growing globally. Data Science, Machine Learning, and other fields are incredibly promising, interesting & have limitless applications. Even though there are many practitioners, there is a lack of skilled specialists in these domains. Data science skill is in great demand since it results in tangible & measurable perks. The significant increase in open data science professions indicates that data science careers are well-positioned for the future.

Statistics from numerous employment portals reveal that the number of data science jobs posted has steadily increased over the years.

There are several paths to becoming a data scientist. The following are the primary steps to pursuing a career in data science.

• - Choose a profession in data science.
• - Continue to learn by developing your data science projects.
• - Learn about data science using our personalized data science training, such as providing resources, unique programs & more.
• - Build your data science portfolio while improving & refining your abilities through our expertise.

For many years, one of the top occupations in India was that of a data scientist. In terms of compensation, job demand, work satisfaction, organizations utilize the phrase "data scientist" to refer to other comparable professions, such as "data analyst."

Demand for data science professionals is rapidly increasing in the market, as the organization maintains itself through data-driven insights. Various firms notice the worth & potential of Big Data knowledge as they thrive to use it to create higher business choices. The supply of skilled applicants is growing at a higher pace. So, it is a great choice to become a data scientist to become a stalwart in the IT industry.

We are happy & proud to say that we have impaneled with numerous small, mid-sized MNCs. Many of these organizations/businesses have data science opportunities widely open for the skillful. Furthermore, we have highly active placement support that assists our students with 100% placement. Our support team will also help by training participants through mock interviews and other skill enhancement training.

The demand for a data scientist is rapidly growing globally. Because the requirement in this domain is so high & the supply of people who can do this job well is limited, data science offers huge pay and perks in a position even at entry-level. Several organizations/companies also refer to data analysts as data scientists. Particular individuals are usually involved in operating with the organization's database.

So coming to this, data science is the field that comprises everything related to data cleansing, data mining, data preparation & data analysis. Big data refers to the amount of data that is difficult to store & process in real-time. These data utilize insights that can lead to better decision-making. Data science algorithms will create a difference with the data insights across industries (like internet searches, search recommendations & advertisements).

Data analysts do not require significant coding abilities, but they should be familiar with analytics tools, data visualization software & data management applications.

Our personalized curriculum is programmed to give a hands-on approach to the students in Data Science. The data science course program teaches the basics of every module, followed by high-intensity practical sessions reflecting the current challenges & needs of the industry that will demand aspirants time & commitment.