Data Science Online Training

The main objective of our Online Data Science Training is to equip the learners with the ability to simplify big data through coding and algorithms to derive a problem-solving solution for global businesses. It is the hottest career in the IT field and the young generation around the world are interested to become data analysts, data scientists, data engineers, AI professionals, ML experts, and Cybersecurity professionals, and so on. Step into the brightest career through Softlogic Systems, the best Online Data Science Training Institute. Following are the reasons why the data science field can bring you a futuristic career with exponential growth for learners.

• Digitalization of global companies is generating a big amount of data through their customers.
• Big data generation around the world creates a huge scope of data analytics
• Organizations find it is easy to manage and interpret data extracted from their services.
• Utilization of software tools and technologies like Java, Python, Hadoop, and Tableau.
• Any undergraduates with basic business skills can learn and master Data Science skills.
• Popular sectors like healthcare, transport, and e-commerce are adopting data science.

The learning of Online Data Science Course at Softlogic helps the candidates to gain expertise in the implementation of quantitative modeling, data analysis techniques, data visualization processes, and programming languages to discover the solution for various real-world problems of organizations. The students will have enhanced knowledge of key technologies in data science and business analytics such as data mining, predictive modeling, machine learning, statistics, artificial intelligence, and critical thinking that help them assist in the decision-making process of global companies. Enjoy the learning benefits at Softlogic through hands-on practices, comprehensive coursework, project implementations, industry-worth certifications, research experiences, and placement assistance in our Online Data Science Training Course. Following are the learning outcomes that you can obtain at the end of our Online Data Science Course.

• Ability to implement quantitative modeling and data analysis techniques for bringing solutions to real-world problems like communicating findings and presenting results.
• Identify and analyze business-related ethical issues for the safety of intellectual property, data integrity, privacy, and data security.
• Implement everyday business activities for well-reasoned data management decisions.
• Able to explain statistical data analysis techniques for business-oriented decision-making.
• Implement the best principles of data science to analyze various business problems.
• Utilization of data mining software to resolve real-world problems.
• Utilize cutting-edge data science tools and technologies that will fit exactly for big data analytics.
• Implement algorithms for developing machine learning processes and artificial intelligence.
• Industry-ready job skills such as teamwork, leadership skills, organizational theory, and decision-making practices.

Our Online Data Science Training with 100% Placement Assistance makes you an expert with industry-ready skills to work in data analytics, data visualization, and data security along with DS-related jobs.

• Industry-standard Coursework
• One-on-one or Small batch size
• Instructor-led live online mode
• Customizable course curriculum
• Industry-valued Certificate
• 4+ real-time project practices
• Instalment Options for fees
• Field Expertized Trainers
• Convenient learning hours
• Free soft skills training
• 100% Placement Assistance
• Free Lifetime Career Guidance

There is no prior knowledge required for learning Online Data Science Course for freshers as we offer them the course from scratch. Our Data Science Course trainers will be allocated as per the convenient learning hours of students around the world and it can be regular classes or weekend classes or fast-track mode. We suggest working professionals have the following prerequisites to enjoy the complete hands-on experience on the updated Data Science concepts.

• Basic knowledge of programming languages
• Fundamental understanding of manual data processing.
• Basic skills in business acumen and decision-making process.

Freshers and working professionals can learn Cloud Computing Course Online at Softlogic as we are providing in-person classes along with other training modes for the students who want to study in our Cloud Computing Training Institute in Chennai. It is beneficial for the students to launch their careers in Information Technology, Business, and Other Cloud-Related Industries. Following are the eligible category for learning Cloud Computing Online Training Course at Softlogic.

• Freshers with a degree in BBA, B.Sc/M.Sc Maths, MBA in Finance, Accounting, Statistics, Commerce, Banking, and any Engineering specialization.,
• Working professionals with experience in IT or non-IT fields like tech support, BPO, or process associate.

Data Science is making remarkable in various industries and the companies are witnessing that the utilization of data science processes brings massive ROI through tools and technologies such as Machine Learning, Python, Java, Artificial Intelligence, and Natural Language Processing, etc. It generates tremendous job opportunities for skilled and certified data science professionals to perform various tasks to retrieve meaningful insights. Learn the best Data Science Online Course with 100% Placement Assistance at Softlogic. Check out the following factors to prove that the launching of a career in data science gives a promising future for global aspirants.

• Multiple Job Profile Options such as Data Scientist, Data Analyst, Research Analyst, Business Analyst, Big Data Engineer, Analytics Manager, and so on.
• Business Organizations will know how and when their products are to be sold by knowing the marketing trends and requirements. It helps businesses with accurate decision-making.
• Highly paid jobs with tremendous career opportunities. According to the survey, the average salary of Data Science Professionals is around $106,000 Per Annum.
• Quickly hiring benefits that the CVs of data science professionals can be hired easily as they will be looking best candidates for the required jobs descriptions with multiple skills.

Our Data Science Online Training Course helps the professionals to get involved for management and industry leaders in their decision-making process. We will train the students with the ability to challenge their staff to focus on the exact issues for adopting the best practices wisely, take direct actions according to trends for defining goals, identify opportunities for data-driven evidence, test the decisions by identifying the target audiences, and recruit the right talent for the organizations. By the end of our Online Data Science Course, the learners can able to apply for the following positions.

• Data Scientist
• Data Analyst
• Data Engineer
• Data Architect
• Data Storyteller
• Machine Learning Scientist
• Machine Learning Engineer
• Business Intelligence Developer
• Database Administrator
• Technology Specialized Roles

Our Online Data Science Course Certification adds value to your resume as it is globally recognized for its quality and performance. It validates your data science skills through a performance tracker and our trainers and placement coach will improve the areas to be focused on. We provide Data Science Course Completion Certification at the end of the online course and we update them in our placement portal for the companies’ reference. Enroll in our Online Data Science Training Institute today to get the learning and career benefits.

• Introduction to R programming
• Types of Objects in R
• Naming standards in R
• Creating Objects in R
• Data Structure in R
• Matrix, Data Frame, String, Vectors
• Understanding Vectors & Data input in R
• Lists, Data Elements
• Creating Data Files using R

• Basic Operations in R – Expressions, Constant Values, Arithmetic, Function Calls, Symbols
• Sub-setting Data
• Selecting (Keeping) Variables
• Excluding (Dropping) Variables
• Selecting Observations and Selection using Subset Function
• Merging Data
• Sorting Data
• Adding Rows
• Visualization using R
• Data Type Conversion
• Built-In Numeric Functions
• Built-In Character Functions
• User Built Functions
• Control Structures
• Loop Functions

• Basic Statistics
• Measure of central tendency
• Types of Distributions
• Anova
• F-Test
• Central Limit Theorem & applications
• Types of variables
• Relationships between variables
• Central Tendency
• Measures of Central Tendency
• Kurtosis
• Skewness
• Arithmetic Mean / Average
• Merits & Demerits of Arithmetic Mean
• Mode, Merits & Demerits of Mode
• Median, Merits & Demerits of Median
• Range
• Concept of Quantiles, Quartiles, percentile
• Standard Deviation
• Variance
• Calculate Variance
• Covariance
• Correlation

• Hypothesis Testing
• Multiple Linear Regression
• Logistic Regression
• Market Basket Analysis
•  Clustering (Hierarchical Clustering & K-means Clustering)
• Classification (Decision Trees)
• Time Series Analysis (Simple Moving Average, Exponential smoothing, ARIMA+)

• Standard Normal Distribution
• Normal Distribution
• Geometric Distribution
• Poisson Distribution
• Binomial Distribution
• Parameters vs. Statistics
• Probability Mass Function
• Random Variable
• Conditional Probability and Independence
• Unions and Intersections
• Finding Probability of dataset
• Terminology in Probability
• Probability Distributions

• Bubble Chart
• Sparklines
• Waterfall chart
• Box Plot
• Line Charts
• Frequency Chart
• Bimodal & Multimodal Histograms
• Histograms
• Scatter Plot
• Pie Chart
• Bar Graph
• Line Graph

• Overview & Terminologies
• What is Machine Learning?
• Why Learn?
• When is Learning required?
• Data Mining
• Application Areas and Roles
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement learning

Steps in developing a Machine Learning application

• Key tasks of Machine Learning
• Modelling Terminologies
• Learning a Class from Examples
• Probability and Inference
• PAC (Probably Approximately Correct) Learning
• Noise
• Complexity-Noise and Model
• Triple Trade-Off
• Association Rules
• Association Measures

• Concept of Regression
• Best Fitting line
• Simple Linear Regression
• Building regression models using excel
• Coefficient of determination (R- Squared)
• Multiple Linear Regression
• Assumptions of Linear Regression
• Variable transformation
• Reading coefficients in MLR
• Multicollinearity
• VIF
• Methods of building Linear regression model in R
• Model validation techniques
• Cooks Distance
• Q-Q Plot
• Durbin- Watson Test
• Kolmogorov-Smirnof Test
• Homoskedasticity of error terms
• Logistic Regression
• Applications of logistic regression
• Odds concept
• Concept of Odds Ratio
• Derivation of logistic regression equation
• Interpretation of logistic regression output
• Model building for logistic regression
• Model validations
• Confusion Matrix
• Concept of ROC/AOC Curve
• KS Test

• Applications of Market Basket Analysis
• What is association Rules
• Overview of Apriori algorithm
• Key terminologies in MBA
• Support
• Confidence
• Lift
• Model building for MBA
• Transforming sales data to suit MBA
• MBA Rule selection
• Ensemble modelling applications using MBA

• Model building using ARIMA, ARIMAX, SARIMAX
• Data De-trending & data differencing
• KPSS Test
• Dickey Fuller Test
• Concept of stationarity
• Model building using exponential smoothing
• Model building using simple moving average
• Time series analysis techniques
• Components of time series
• Prerequisites for time series analysis
• Concept of Time series data
• Applications of Forecasting

• Understanding the Concept
• Internal decision nodes
• Terminal leaves.
• Tree induction: Construction of the tree
• Classification Trees
• Entropy
• Selecting Attribute
• Information Gain
• Partially learned tree
• Overfitting
• Causes for over fitting
• Overfitting Prevention (Pruning) Methods
• Reduced Error Pruning
• Decision trees – Advantages & Drawbacks
• Ensemble Models

• Parametric Methods Recap
• Clustering
• Direct Clustering Method
• Mixture densities
• lasses v/s Clusters
• Hierarchical Clustering
• Dendogram interpretation
• Non-Hierarchical Clustering
• K-Means
• Distance Metrics
• K-Means Algorithm
• K-Means Objective
• Color Quantization
• Vector Quantization

• Tableau Introduction
• Data connection to Tableau
• Calculated fields, hierarchy, parameters, sets, groups in Tableau
• Various visualizations Techniques in Tableau
• Map based visualization using Tableau
• Reference Lines
• Adding Totals, sub totals, Captions
• Advanced Formatting Options
• Using Combined Field
• Show Filter & Use various filter options
• Data Sorting
• Create Combined Field
• Table Calculations
• Creating Tableau Dashboard
• Action Filters
• Creating Story using Tableau

• Clustering using Tableau
• Time series analysis using Tableau
• Simple Linear Regression using Tableau

• Integrating R code with Tableau
• Creating statistical model with dynamic inputs
• Visualizing R output in Tableau
• Case Studies
• 1- Real time project with Twitter Data Analytics
• 2- Real time project with Google Finance
• 3- Real time project with IMDB Website