Data Science – ML & A.I
In this Data Science course in Nigeria, you will learn best practices and conventions in Data Science and apply them to create effective and compelling and readable Visualization for businesses and start ups. From there you will learn Statistics for Data Science, My SQL, Python, Machine Learning and Artificial Intelligence. LS Academy is one of the best place in Lagos Nigeria to learn Data Science. This class is 100% practical with hands on projects to speed up your learning. Get a certificate and compulsory internship after your training.
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INTRODUCTION TO DATA SCIENCEWhere data science fits into today’s society.0sAnalysis vs Analytics0sIntro to Business Analytics, Data Analytics, and Data Science0sBusiness Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI)0sRelationship Between Different Data Science Field0sTraditional data, Big Data, BI, Traditional Data Science and ML applied0sPurpose Of Each Data Science Field0sCommon Data Science Techniques0sTraditional Data: Techniques0sTraditional Data: Real-life Examples0sBig Data: Techniques0sBig Data: Real-life Examples0s
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Introduction to Python Programming
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Python Basics
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Data Structures and Collections
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Object-Oriented Programming (OOP) in Python
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File Handling and I/O Operations
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Libraries and Modules
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Error Handling and Debugging
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Introduction to Web Scraping
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Introduction to GUI Development
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SQL AND DATABASES
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Getting Started & Installation
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Creating Databases & Tables
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Inserting Data
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CRUD Basics
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String Functions
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Refining Selections
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Aggregate Functions
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Revisiting Data Types
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Comparison & Logical Operators
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Constraints & ALTER TABLE
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One to Many & Joins
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Many to Many
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Views & Modes
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Window Functions
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Instagram Database Clone
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Working With Lots of IG Data
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Creating and formatting a table visualisationInstalling Power BI Desktop0sVisualizations0sImporting from Excel, and Creating our first visualization0sExploring Power BI Desktop – Report view0sExploring Power BI Desktop – Data view0sFocus mode and Different visualizations0sSaving visualization to the Desktop and to the Power BI service0sCreating and formatting a table visualization0s
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Creating different visualizations: Matrices and bar chartsMatrix0sDrill down data, see data and records, and export data0sStacked bar charts and switch theme for reports0sBar Chart formatting, including continuous versus categorical axes0sConfigure interactions between visual (Edit interactions)0sClustered and 100% Stacked bar charts0sLine and area charts0sCombo charts (Line and column charts)0sMatrices and Bar charts0s
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Adding more control to your visualisationsAdding Text boxes, Images and Shapes0sVisual level, page level and report level filters – basic filters0sAdvanced Filtering0sFilter Top N Items0sSlicer0sSynchronizing slicers to multiple pages0sSlicer Warning0sAdding more control to your visualizations – Filters and slicers0sSort visuals0sConfigure small multiples0sUse Bookmarks for reports0sGroup and layer visuals by using the Selection pane0sAdding more control to your visualizations0sDrillthrough0sButtons and Actions0sPage Navigation and Drill through actions0sEnable Natural Language Queries0sTooltip Pages0sPage and Bookmark Navigator0sAdding more control to your visualizations – Part 30s
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Other visualizations
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Mapping
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Measure performance by using KPIs, gauges and cards
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Identify patterns and trends and Other Visualization ItemsDefine quick measures0sExport report data0sCreate reference lines by using Analytics pane, including the Forecast feature0sUse error bars0sIdentify patterns and trends and Other Visualization Items – Part 10sIdentify outliers0sUse clustering0sUse Anomaly Detection0sAdd a Smart Narrative visual0sUse groupings and binnings0sIdentify patterns and trends and Other Visualization Items0sUse the AI Visual Key Influencers to explore dimensional variances0sUse the Analyze feature in Power BI0sUse the AI Visual decomposition tree visual to break down a measure0sCreating a paginated report0sExploring Power BI Report Builder0s
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Get and Transform Data:
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Get Data – Home
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Getting Multiple files
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Transform Menu
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Transform – Text and Numbers
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Transform – Dates and Time
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Add Columns, View and Help Menus
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View and Help menus and advanced functionality
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Get other types of dataIntroduction to SQL Server0sImporting database data into Power BI, and Query Folding0sExpanding multiple tables in SQL Server0sSelect a storage mode0sImporting data from SQL Server Analysis Services (SSAS)0sSetting up Azure SQL Database0sUsing Azure SQL Database in Power BI0sUse the Microsoft Dataverse0sConfigure data loading0s
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Get and Transform
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Creating a Data Model
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DAX Functions
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Statistical functions
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Mathematical functions
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Text functions
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Information Functions
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Filter and Value Functions
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Time Intelligence FunctionsDate and Time Functions0sFIRSTDATE, LAST DATE0sStart of… and End of…0sPrevious… and Next…0sDATESINPERIOD0sDATESMTD, DATESQTD, DATESYTD, TOTALMTD, TOTALQTD, TOTALYTD0sOpening Balance and Closing Balance0sSemi-additive Measures0sSAMEPERIODLASTYEAR and PARALLELPERIOD0sOther Time Intelligence Functions0s
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Other Modeling and DAX TopicsCreate calculated tables0sCreate a common date table0sDefine role-playing dimensions0sResolve many-to-many relationships – Joint Bank Accounts0sResolve many-to-many relationships – Different types of granularity0sImprove cardinality levels through summarization0sChanging data types0sIdentify poorly performing measures, relationships, and visuals0sThe Optimize menu0s
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STATISTICS FOR DATA SCIENCE
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VERSION CONTROL – GIT AND GITHUB
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PYTHON FOR DATA SCIENCE
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Why Python Programming
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Data Types and Operators
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Data Structures in Python
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Control Flow
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Functions
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Scripting
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NUMPY FOR DATA SCIENCE
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PANDAS FOR DATA WRANGLINGIntroduction to Pandas for data manipulation and analysisSeries and DataFrame objectsLoading data into PandasData exploration and basic statisticsData cleaning and handling missing valuesData filtering, selection, and sortingData visualization with PandasWhat is data wrangling and why is it important?Data acquisition methods (reading from files, web scraping, APIs)Data cleaning techniques (handling missing values, dealing with duplicates)Data transformation (reshaping data, merging and joining datasets)Data aggregation and groupingData normalisation and scalingDealing with outliersHandling categorical data (encoding and one-hot encoding)Date and time data manipulationIntroduction to data quality and validationAdvanced Pandas techniques for data manipulation (pivot tables, melt, stack, unstack)Combining and merging DataFrames (concatenation, merging on keys)Data filtering and selection (loc, iloc)Using Pandas functions to clean and transform dataHandling missing data with PandasApplying custom functions to data using Pandas
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MATPLOTLIBIntroduction to Matplotlib and its role in data visualisationBasic plotting with Matplotlib (line plots, scatter plots, bar charts)Customising plots (labels, titles, legends)Subplots and figure customizationAdvanced plotting techniques (histograms, box plots, heatmaps)Saving and exporting plots in different formats
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SEABORNIntroduction to Seaborn and its advantages over MatplotlibSeaborn’s aesthetics and built-in themesCreating statistical visualisations (distribution plots, categorical plots)Visualizing relationships (scatter plots, pair plots, heatmaps)Advanced customization and styling in SeabornCombining Seaborn with Pandas DataFrames for effective data exploration
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VISUALIZATION
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MACHINE LEARNING
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ADVANCED REGRESSIONIntroduction To Machine Learning0sPredictive Modelling And Classification0sAssessing Accuracy And The Train-Test Split0sStatistical Learning0sLinear Models0sLeast Squares Regression0sSplitting Datasets0sThe Train/Test Split0sMultiple Linear Regression0sVariables And Variable Selection0sFeature Engineering0sSaving And Restoring Models0sRegularisation – Data Scaling0sRegularisation : Ridge Regression0sRegularisation : LASSO Regression0sDecision Trees0sBias-Variance Tradeoff0sParametric Methods, Ensembling And Bootstrapping0sRandom Forests0s
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ADVANCED CLASSIFICATIONAdvanced Classification0sNatural Language Processing0sHow Machines Understand Language0sLogistic Regression0sIntro To Binary Classification Using Logistic Regression0sClassification Metrics0sModel Improvements0sImproving Classification Models0sDealing With Imbalanced Data0sTree-Based Classification Methods0sTraining A Decision Tree0sTree-Based Methods For Classification0sSupport Vector Classification0sSupport Vector Machines0sNearest Neighbours And Naive Bayes0sKNNs And Naive Bayes0sHyperparameter Tuning & Model Validation0sHyperparameters And Model Validation0sNeural Network Classifiers0sClassifier Model Selection0sBuild All The Classifiers0s
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UNSUPERVISED LEARNING
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Project and Portfolio Collation
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Projects (Fragments)Data Analysis of a CSV File0sStock Portfolio Analysis0sCustomer Segmentation0sTime Series Forecasting0sMovie Recommender System0sE-commerce Sales Analysis0sData Cleaning and Transformation Tool0sHouse Price Prediction0sEnergy Consumption Forecasting0sStock Price Prediction0sCustomer Churn Prediction0sSentiment Analysis on Social Media0sImage Classification0sCustomer Segmentation0sAnomaly Detection in Network Traffic0sTopic Modeling for Text Data0sMarket Basket Analysis0s
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Capstone Projects
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CV/Resume Review
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Career Support & Mentorship
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Mandatory Internship (1-3 months)
You will learn current best practices and conventions in Data Science and apply them to create effective and compelling and readable Visualization for businesses and start up. From there you will learn Statistics for Data Science, My SQL, Python for Data Science, Machine Learning and Artificial Intelligence. Classes are always fun and insightful so go ahead, enroll and join us.
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What you’ll learn
- Data Science Fundamentals and the business value.
- Discover the importance of refining and editing data, and what can go wrong if you don’t do it correctly.
- Examine ways to connect with your audience by personalizing your content.
- Ideation techniques.
- Learn Python for Data Science.
- Statistics and Prediction
- Machine Learning
This Data Science Course incorporates visuals as early as possible rather than adding them on at the end. Within the first few of sections, you’ll start to formulate your first plots! Additionally, unlike most other courses where you must spend hours dealing with boring, false data (colors, animals, etc.) before you ever see your first real dataset, we begin using real datasets right away. What are you waiting for? Enroll right away!!!
Take Data Science Crash Course | Data Science Training in Lagos
What's included
- 4 months duration + 3 months internship
- Certificate
- 4 Article
- Live Tutor
- Lifetime access
